importing¶
In [1]:
#@title drive
# from google.colab import drive
# drive.mount('/content/drive')
In [2]:
#@title pandas and read specific excel
import pandas as pd
df = pd.read_excel('0_8001-8500.xlsx', sheet_name='Sheet1')
In [3]:
#@title warning
import warnings
warnings.filterwarnings("ignore")
first look¶
In [4]:
#@title set option to display max
pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', None)
In [5]:
#@title display sample original dataset
display(df.sample(4))
| S. NO. | REGISTRATION DATE | INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 106 | 8107.0 | 2076-08-10 | ASIANINTERNATIONALREGENCYPVT.LTD. | RUPANDEHI | 2380000000 | 2340000000 | 40000000 | HOTEL176BEDSRESTAURANT225SEATS | 97 | TOURISM | LARGE | 2000\nKVA | Local100% |
| 159 | 8160.0 | 2076-09-22 | SHUNYUANINTERNATIONALCARGOPVT.LTD. | KATHMANDU | 150000000 | 145000000 | 5000000 | INTERNATIONALCARGOHANDLING16000MT | 63 | SERVICE | MEDIUM | 10KVA | Foreign100% |
| 251 | 8252.0 | 2077-03-31 | MONA\nHYDROPOWERLIMITED | MYAGDI | 997693380 | 969371270 | 28322110 | HYDROELECTRICITY5.5M.W. | 39 | ENERGY | LARGE | 50\nK.V.A. | Local100% |
| 28 | 8029.0 | 2076-05-20 | CHENXINGRESTAURANTPVT.LTD. | KATHMANDU | 150000000 | 110000000 | 40000000 | RESTAURANT200SEATS | 25 | TOURISM | MEDIUM | 25KVA | Foreign100% |
In [6]:
#@title drop empty rows
df.dropna(how='all', inplace=True)
In [7]:
# @title shape
df.shape
Out[7]:
(500, 13)
In [8]:
#@title query to specific
df.query('`S. NO.` == 8377')
Out[8]:
| S. NO. | REGISTRATION DATE | INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 376 | 8377.0 | 2077-11-11 | SEPLIHYDROPOWERDEVELOPMENTCOMPANYPVT.LTD. | OKHALDHUNGA | 890136907 | 881500000 | 8636907 | HYDROELECTRICITY5M.W. | 11 | ENERGY | LARGE | 60\nK.V.A. | Local100% |
In [9]:
#@title copy df
df1 = df.copy(deep=True)
In [10]:
#@title info
df1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 500 entries, 0 to 499 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 S. NO. 500 non-null float64 1 REGISTRATION DATE 500 non-null datetime64[ns] 2 INDUSTRY NAME 500 non-null object 3 DISTRICT 486 non-null object 4 TOTAL CAPITAL 500 non-null int64 5 FIXED CAPITAL 500 non-null int64 6 WORKING CAPITAL 500 non-null int64 7 PRODUCT AND ANNUAL CAPACITY 500 non-null object 8 EMPLOYMENT 500 non-null int64 9 CATEGORY 500 non-null object 10 SCALE 500 non-null object 11 POWER 500 non-null object 12 % OF INVESTMENT 500 non-null object dtypes: datetime64[ns](1), float64(1), int64(4), object(7) memory usage: 50.9+ KB
In [11]:
#@title display non available
display(df1.isna().sum())
S. NO. 0 REGISTRATION DATE 0 INDUSTRY NAME 0 DISTRICT 14 TOTAL CAPITAL 0 FIXED CAPITAL 0 WORKING CAPITAL 0 PRODUCT AND ANNUAL CAPACITY 0 EMPLOYMENT 0 CATEGORY 0 SCALE 0 POWER 0 % OF INVESTMENT 0 dtype: int64
In [12]:
#@title drop it
# df1 = df1.dropna()
In [13]:
#@title describe df1
display(df1.describe())
| S. NO. | REGISTRATION DATE | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | EMPLOYMENT | |
|---|---|---|---|---|---|---|
| count | 500.000000 | 500 | 5.000000e+02 | 5.000000e+02 | 5.000000e+02 | 500.000000 |
| mean | 8250.500000 | 2076-11-05 13:14:52.800000 | 6.454023e+08 | 5.805375e+08 | 6.486473e+07 | 63.918000 |
| min | 8001.000000 | 1978-03-01 00:00:00 | 2.135500e+06 | 8.855000e+05 | 9.000000e+05 | 0.000000 |
| 25% | 8125.750000 | 2076-08-18 00:00:00 | 1.100000e+08 | 9.100000e+07 | 9.419560e+06 | 30.000000 |
| 50% | 8250.500000 | 2077-03-28 12:00:00 | 2.000000e+08 | 1.513500e+08 | 2.585000e+07 | 45.000000 |
| 75% | 8375.250000 | 2077-11-03 18:00:00 | 3.676000e+08 | 2.565223e+08 | 6.212541e+07 | 70.000000 |
| max | 8500.000000 | 2078-05-18 00:00:00 | 1.762406e+10 | 1.757100e+10 | 2.471689e+09 | 550.000000 |
| std | 144.481833 | NaN | 1.526715e+09 | 1.494585e+09 | 1.659136e+08 | 65.985236 |
In [14]:
#@title registration data info and describe
display(df1['REGISTRATION DATE'].info())
display(df1['REGISTRATION DATE'].describe())
<class 'pandas.core.series.Series'> RangeIndex: 500 entries, 0 to 499 Series name: REGISTRATION DATE Non-Null Count Dtype -------------- ----- 500 non-null datetime64[ns] dtypes: datetime64[ns](1) memory usage: 4.0 KB
None
count 500 mean 2076-11-05 13:14:52.800000 min 1978-03-01 00:00:00 25% 2076-08-18 00:00:00 50% 2077-03-28 12:00:00 75% 2077-11-03 18:00:00 max 2078-05-18 00:00:00 Name: REGISTRATION DATE, dtype: object
In [15]:
#@title scale info and value count
display(df1['SCALE'].info())
display(df1['SCALE'].value_counts())
<class 'pandas.core.series.Series'> RangeIndex: 500 entries, 0 to 499 Series name: SCALE Non-Null Count Dtype -------------- ----- 500 non-null object dtypes: object(1) memory usage: 4.0+ KB
None
SCALE SMALL 216 MEDIUM 183 LARGE 101 Name: count, dtype: int64
In [16]:
#@title category info and value count
display(df1['CATEGORY'].info())
display(df1['CATEGORY'].value_counts())
<class 'pandas.core.series.Series'> RangeIndex: 500 entries, 0 to 499 Series name: CATEGORY Non-Null Count Dtype -------------- ----- 500 non-null object dtypes: object(1) memory usage: 4.0+ KB
None
CATEGORY MANUFACTURING 155 TOURISM 129 SERVICE 83 ENERGY 72 INFORMATION TECHNOLOGY 25 AGR0AND\nFORESTRY 20 AGRO AND FORESTRY 13 INFRASTRUCTURE 2 MINERAL 1 Name: count, dtype: int64
In [17]:
#@title dsitrict info and value counts
print(df1['DISTRICT'].info())
display(df1['DISTRICT'].value_counts())
print(df1['DISTRICT'].nunique())
<class 'pandas.core.series.Series'> RangeIndex: 500 entries, 0 to 499 Series name: DISTRICT Non-Null Count Dtype -------------- ----- 486 non-null object dtypes: object(1) memory usage: 4.0+ KB None
DISTRICT KATHMANDU 157 LALITPUR 37 KASKI 25 RUPANDEHI 21 BARA 19 NAWALPARASI 19 MORANG 17 CHITWAN 15 BHAKTAPUR 12 SUNSARI 12 JHAPA 11 PARSA 10 SINDHUPALCHOWK 9 DHADING 8 KAILALI 8 GORKHA 7 NUWAKOT 7 KAPILBASTU 7 MAKWANPUR 7 DOLKHA 6 SANKHUWASABHA 6 SOLUKHUMBU 6 MYAGDI 6 BANKE 6 KAVRE 6 LAMJUNG 5 DHANUSHA 5 TAPLEJUNG 4 ILAM 3 RASUWA 2 MAHOTTARI 2 DANG 2 OKHALDHUNGA 2 MANANG 2 SARLAHI 2 RAUTAHAT 2 KHOTANG 2 TANAHU 2 BARDIYA 1 BAJURA 1 RUKUM 1 BAGLUNG 1 SIRAHA 1 SINDHULI 1 KANCHANPUR 1 Name: count, dtype: int64
45
In [18]:
#@title % of investment nunique and calue count
print(df1['% OF INVESTMENT'].nunique())
display(df1['% OF INVESTMENT'].value_counts())
26
% OF INVESTMENT Local100% 210 Foreign100% 121 Foreign-100% 75 Local-100% 59 Local-40%\nForeign-60% 4 Local1000Zo 4 Local-20%\nForeign-80% 3 Local-51%\nForeign-49% 3 Local-10%\nForeign-90% 2 Local-6.25%Foreign-93.75% 2 Local-15%\nForeign-85% 2 Local-50%\nForeign-50% 1 Local-36%\nForeign-64% 1 Local-5.67%Foreign-94.33% 1 Local-34.221%\nForeign-65.779% 1 Local-9.91%Foreign-90.090/o 1 Local50%\nForeign50% 1 Local-15%Foreign-85% 1 Local-13.04%Foreign-86.96% 1 Local1000/o 1 Local-15.24%Foreign-84.76% 1 Local-49%\nForeign-51% 1 Local-16.667%\nForeign-83.333% 1 Local-20.29%Foreign-79.71% 1 Local-66.42%Foreign-33.58% 1 Local-6%\nForeign-94% 1 Name: count, dtype: int64
In [19]:
#@title power describe info
display(df1['POWER'].describe())
display(df1['POWER'].info())
count 500 unique 73 top 100KVA freq 40 Name: POWER, dtype: object
<class 'pandas.core.series.Series'> RangeIndex: 500 entries, 0 to 499 Series name: POWER Non-Null Count Dtype -------------- ----- 500 non-null object dtypes: object(1) memory usage: 4.0+ KB
None
clean¶
In [20]:
#@title copy to clean
df2=df1.copy(deep=True)
In [21]:
#@title check data type
df2.dtypes
Out[21]:
S. NO. float64 REGISTRATION DATE datetime64[ns] INDUSTRY NAME object DISTRICT object TOTAL CAPITAL int64 FIXED CAPITAL int64 WORKING CAPITAL int64 PRODUCT AND ANNUAL CAPACITY object EMPLOYMENT int64 CATEGORY object SCALE object POWER object % OF INVESTMENT object dtype: object
In [22]:
#@title drop SNo
df2 = df2.drop(['S. NO.'], axis=1)
In [23]:
#@title fix date column extract year and month
# convert to a string
df2['REGISTRATION DATE'] = df2['REGISTRATION DATE'].astype(str)
# date part
df2['MONTH'] = df2['REGISTRATION DATE'].str[5:7].astype(int)
# year part
df2['YEAR'] = df2['REGISTRATION DATE'].str[:4]
df2['YEAR'] = pd.to_numeric(df2['YEAR'], errors='coerce')
df2['YEAR'] = df2['YEAR'].astype('Int64')
# drop original
df2 = df2.drop('REGISTRATION DATE', axis=1)
In [24]:
#@title regular expression
import re
In [25]:
#@title remove repeating liability of stakeholder and cleaninf
df2['INDUSTRY NAME'] = df2['INDUSTRY NAME'].replace(r'PVT\.|LTD\.|\n', '', regex=True) \
.replace(r'\s+', ' ', regex=True) \
.str.strip()
In [26]:
#@title check investment
print(df2['% OF INVESTMENT'].unique())
['Foreign-100%' 'Local100%' 'Local-100%' 'Local-6.25%Foreign-93.75%' 'Foreign100%' 'Local-66.42%Foreign-33.58%' 'Local-51%\nForeign-49%' 'Local-20.29%Foreign-79.71%' 'Local-16.667%\nForeign-83.333%' 'Local-49%\nForeign-51%' 'Local-40%\nForeign-60%' 'Local-10%\nForeign-90%' 'Local1000Zo' 'Local-15%Foreign-85%' 'Local-15.24%Foreign-84.76%' 'Local1000/o' 'Local-20%\nForeign-80%' 'Local-13.04%Foreign-86.96%' 'Local-36%\nForeign-64%' 'Local-9.91%Foreign-90.090/o' 'Local-15%\nForeign-85%' 'Local-5.67%Foreign-94.33%' 'Local50%\nForeign50%' 'Local-50%\nForeign-50%' 'Local-34.221%\nForeign-65.779%' 'Local-6%\nForeign-94%']
In [27]:
#@title turn % of investment to standard format
def clean_investment_percentage(value):
value = str(value).replace('\n', ' ').replace('0Zo', ' ').replace('0/o', ' ').replace('/', ' ')
value = re.sub(r'\s+', ' ', value).strip()
local_match = re.search(r'Local\s*[-–—:\s]?\s*([\d.,]+)%', value)
foreign_match = re.search(r'Foreign\s*[-–—:\s]?\s*([\d.,]+)%', value)
local_pct = local_match.group(1).replace(',', '') if local_match else '0'
foreign_pct = foreign_match.group(1).replace(',', '') if foreign_match else '0'
return f"Local - {local_pct}%, Foreign - {foreign_pct}%"
df2['% OF INVESTMENT'] = df2['% OF INVESTMENT'].apply(clean_investment_percentage)
In [28]:
df2['% OF INVESTMENT'].value_counts()
Out[28]:
% OF INVESTMENT Local - 100%, Foreign - 0% 269 Local - 0%, Foreign - 100% 196 Local - 0%, Foreign - 0% 5 Local - 40%, Foreign - 60% 4 Local - 15%, Foreign - 85% 3 Local - 51%, Foreign - 49% 3 Local - 20%, Foreign - 80% 3 Local - 50%, Foreign - 50% 2 Local - 10%, Foreign - 90% 2 Local - 6.25%, Foreign - 93.75% 2 Local - 66.42%, Foreign - 33.58% 1 Local - 36%, Foreign - 64% 1 Local - 34.221%, Foreign - 65.779% 1 Local - 5.67%, Foreign - 94.33% 1 Local - 9.91%, Foreign - 0% 1 Local - 15.24%, Foreign - 84.76% 1 Local - 13.04%, Foreign - 86.96% 1 Local - 49%, Foreign - 51% 1 Local - 16.667%, Foreign - 83.333% 1 Local - 20.29%, Foreign - 79.71% 1 Local - 6%, Foreign - 94% 1 Name: count, dtype: int64
In [29]:
#@title check for null, empty, nan
# Check for null values in '% OF INVESTMENT'
print(df2['% OF INVESTMENT'].isnull().sum())
# Check for empty strings in '% OF INVESTMENT'
print((df2['% OF INVESTMENT'] == '').sum())
# Check for NaN values in '% OF INVESTMENT'
print(df2['% OF INVESTMENT'].isna().sum())
0 0 0
In [30]:
#@title only keep numbers from power
df2['POWER'] = df2['POWER'].apply(lambda x: re.sub(r'\D', '', str(x)))
In [31]:
#@title clean power only keep numeric
df2['POWER'] = df2['POWER'].str.replace('\n', ' ', regex=False).str.replace(r'\s+', ' ', regex=True).str.replace('KVA', '', regex=False).str.strip().astype(int)
In [32]:
#@title clean category
df2['CATEGORY'] = df2['CATEGORY'].str.replace('AGR0AND\nFORESTRY', 'AGRO AND FORESTRY')
print(df2['CATEGORY'].unique())
['SERVICE' 'ENERGY' 'INFORMATION TECHNOLOGY' 'TOURISM' 'MANUFACTURING' 'AGRO AND FORESTRY' 'MINERAL' 'INFRASTRUCTURE']
In [33]:
#@title display clean dataset
display(df2)
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | HUALICONSTRUCTIONANDENGINEERING | BHAKTAPUR | 150000000 | 87000000 | 63000000 | VARIOUSKINDSOFCONSTRUCTIONWORKS(CONSTRUCTIONRE... | 88 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 4 | 2076 |
| 1 | CHISANGHYDRO | MORANG | 304505000 | 296587693 | 7917307 | Hydroelectricproduction1.8MW | 31 | ENERGY | LARGE | 30 | Local - 100%, Foreign - 0% | 4 | 2076 |
| 2 | MOKSHAINTERNATIONALCARGO | KATHMANDU | 50000000 | 46000000 | 4000000 | INTERNATIONALCARGOHANDLING12000MT | 50 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 5 | 2076 |
| 3 | TENGFEICONSTRUCTIONCOMPANY | KATHMANDU | 300000000 | 227000000 | 73000000 | CONSTRUCTIONWORKSOFVARIOUSTYPE1000000000L.S | 375 | SERVICE | MEDIUM | 100 | Local - 0%, Foreign - 100% | 5 | 2076 |
| 4 | S.W.SOFTWARE | LALITPUR | 250000000 | 227000000 | 23000000 | SOFTWAREDEVELOPMENT350PACKAGE | 83 | INFORMATION TECHNOLOGY | MEDIUM | 25 | Local - 0%, Foreign - 100% | 5 | 2076 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | AGRIVASTUCOLDST0RAGE | KAPILBASTU | 234192207 | 160104320 | 74087887 | COLDST0RAGE1800MT.PRODUCTIONPROCESSINGAND\nSTO... | 27 | AGRO AND FORESTRY | MEDIUM | 300 | Local - 100%, Foreign - 0% | 5 | 2078 |
| 496 | GHORAHICEMENTINDUSTRYLIMITED(CEMENTPACKEGING) | BANKE | 210338607 | 167189200 | 43149407 | CEMENT120000MT. | 48 | SERVICE | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 5 | 2078 |
| 497 | S.S.PRODUCTS | SARLAHI | 2135500 | 885500 | 1250000 | PANMASALA(PLAINJARDA)14250KG. | 22 | MANUFACTURING | SMALL | 15 | Local - 100%, Foreign - 0% | 5 | 2078 |
| 498 | SHIVASHAKTIOILANDFATS(OIL) | BARA | 500000000 | 300000000 | 200000000 | REFINEDSOYABEANOIL30000MT.REFINEDSUNFLOWEROIL3... | 60 | MANUFACTURING | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 5 | 2078 |
| 499 | SHERPAOUTDOORSPORTSGOODSINDUSTRIES | KATHMANDU | 170000000 | 150000000 | 20000000 | READYMADEGARMENT&TREKKINGGOODSSUCHASSLEEPINGBA... | 200 | MANUFACTURING | SMALL | 300 | Local - 100%, Foreign - 0% | 5 | 2078 |
500 rows × 13 columns
In [34]:
#@title save dataset
df2.to_csv('1_cleaned.csv', index=False)
In [35]:
#@title value count of scale
display(df2['SCALE'].value_counts())
SCALE SMALL 216 MEDIUM 183 LARGE 101 Name: count, dtype: int64
feature engineering¶
In [36]:
#@title upload file to temporary colab runtime
# from google.colab import files
# uploaded = files.upload()
# for filename, content in uploaded.items():
# with open(filename, 'wb') as f:
# f.write(content)
In [37]:
#@title import clean csv
# import pandas as pd
df2 = pd.read_csv('1_cleaned.csv')
In [38]:
#@title view list of columns
columns = df2.columns.tolist()
print("Columns:", columns)
Columns: ['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT', 'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR']
In [39]:
#@title display sample of dataset
for column in columns:
print(f"\nColumn: {column}")
display(df2[column].sample(4))
Column: INDUSTRY NAME
181 GURANSHERBACEUTICALS 21 ZEGALNEPAL 247 JALSHAKTIHYDROCOMPANY 76 RUIYONGRESTAURANT Name: INDUSTRY NAME, dtype: object
Column: DISTRICT
14 SUNSARI 378 MAKWANPUR 216 KATHMANDU 243 KATHMANDU Name: DISTRICT, dtype: object
Column: TOTAL CAPITAL
4 250000000 301 250000000 303 4079657696 199 833543925 Name: TOTAL CAPITAL, dtype: int64
Column: FIXED CAPITAL
266 145000000 376 881500000 92 4500000 380 26500000 Name: FIXED CAPITAL, dtype: int64
Column: WORKING CAPITAL
106 40000000 232 41475000 300 96600000 85 8000000 Name: WORKING CAPITAL, dtype: int64
Column: PRODUCT AND ANNUAL CAPACITY
272 INTERNATIONALCARGOHANDLINGSERVICE20000M.T. 304 HYDROELECTRICITY22.9M.W. 382 LABELIMPRESSION(1.15MTOR75000SEATPERDAY)318M.T. 336 VATI/GUGGUL/CAPSULE/TABLET900000KG.SYRUP/ASVA\... Name: PRODUCT AND ANNUAL CAPACITY, dtype: object
Column: EMPLOYMENT
385 39 346 94 290 68 75 100 Name: EMPLOYMENT, dtype: int64
Column: CATEGORY
355 MANUFACTURING 40 MANUFACTURING 174 INFORMATION TECHNOLOGY 2 SERVICE Name: CATEGORY, dtype: object
Column: SCALE
357 SMALL 349 SMALL 37 MEDIUM 153 SMALL Name: SCALE, dtype: object
Column: POWER
4 25 322 100 110 50 484 1000 Name: POWER, dtype: int64
Column: % OF INVESTMENT
390 Local - 100%, Foreign - 0% 201 Local - 100%, Foreign - 0% 218 Local - 0%, Foreign - 100% 169 Local - 100%, Foreign - 0% Name: % OF INVESTMENT, dtype: object
Column: MONTH
210 11 82 7 196 11 145 9 Name: MONTH, dtype: int64
Column: YEAR
490 2078 101 2076 408 2077 247 2077 Name: YEAR, dtype: int64
In [40]:
#@title random
import random
In [41]:
print(df2['DISTRICT'].nunique())
45
In [42]:
#@title check for district without value
display(df2[df2['DISTRICT'].isnull()])
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | SPRINGHILLHOTEL | NaN | 50000000 | 47000000 | 3000000 | HOTEL22BEDSRESTAURANT40SEATS | 29 | TOURISM | SMALL | 60 | Local - 0%, Foreign - 100% | 8 | 2076 |
| 249 | HIMALAYANRENEWABLEOILINDUSTRY | NaN | 250000000 | 224492402 | 25507598 | PETROL-1485KLDIESEL-1485KLLUBRICANTS(BYPRODUCT... | 42 | MANUFACTURING | MEDIUM | 2000 | Local - 20%, Foreign - 80% | 3 | 2077 |
| 250 | NEBULAENERGY | NaN | 150000000 | 105000000 | 45000000 | ELECTRICVEHICLESASSEMBLING300NOS. | 67 | MANUFACTURING | SMALL | 500 | Local - 100%, Foreign - 0% | 3 | 2077 |
| 256 | PRISTINENEPALTERMINALS | NaN | 80000000 | 64000000 | 16000000 | CONTAINERS14550MTBULK&BREAKBULK9050MT | 74 | SERVICE | SMALL | 100 | Local - 36%, Foreign - 64% | 4 | 2077 |
| 285 | SAMADHIVILLAGE | NaN | 50000000 | 37500000 | 12500000 | HOTEL20BEDSRESTAURANT100SEATS | 17 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 6 | 2077 |
| 309 | SETIKHOLAHYDROPWER | NaN | 5005648000 | 4945816000 | 59832000 | HYDROELECTRICITY22M.W. | 36 | ENERGY | LARGE | 100 | Local - 100%, Foreign - 0% | 6 | 2077 |
| 342 | PATHIVARAMATAFERTILIZERINDUSTRIES | NaN | 219800000 | 162400000 | 57400000 | CHEMICALFERTILIZER3000M.T. | 45 | MANUFACTURING | MEDIUM | 900 | Local - 100%, Foreign - 0% | 9 | 2077 |
| 345 | SWARARETREAT | NaN | 53000000 | 41500000 | 11500000 | HOTEL40BEDSRESTAURANT60SEATS | 38 | TOURISM | SMALL | 50 | Local - 5.67%, Foreign - 94.33% | 9 | 2077 |
| 351 | BOLBOMFEEDINDUSTRIES | NaN | 340000000 | 213000000 | 127000000 | ANIMALFEED(PELLET/DISC)73000M.T. | 55 | AGRO AND FORESTRY | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 9 | 2077 |
| 432 | MANDAKINIHYDROPOWERCOMPANYLIMITED-1 | NaN | 569981000 | 565307297 | 4673703 | HYDROELECTRICITY2.9M.W. | 40 | ENERGY | LARGE | 20 | Local - 100%, Foreign - 0% | 1 | 2078 |
| 434 | ADIRATEXTILEINDUSTRIES | NaN | 200000000 | 137100000 | 62900000 | VARIOUSFABRICS4200000SQ.M | 59 | MANUFACTURING | SMALL | 800 | Local - 100%, Foreign - 0% | 1 | 2078 |
| 435 | ADIRAFLOORS | NaN | 150000000 | 114000000 | 36000000 | PVCFLO0RINGSHEET1400MT. | 46 | MANUFACTURING | SMALL | 800 | Local - 100%, Foreign - 0% | 1 | 2078 |
| 492 | HIMALI HYDROFUND | NaN | 1900000000 | 1850000000 | 50000000 | HYDROELECTRICITY$MW | 31 | ENERGY | LARGE | 25 | Local - 100%, Foreign - 0% | 4 | 2078 |
| 494 | SHIVAMVAGOILLTD | NaN | 750000000 | 400000000 | 350000000 | VANASPATIGHEEANDREFINED0IL(SOYABEANSUNFLOWERRB... | 72 | MANUFACTURING | MEDIUM | 1500 | Local - 100%, Foreign - 0% | 5 | 2078 |
In [43]:
# @title list of districts to randomly impute
districts_to_impute = ['DOLPA', 'MUGU', 'HUMLA',
'JUMLA', 'SALYAN', 'JAJARKOT',
'DAILEKH', 'SURKHET', 'KALIKOT']
In [44]:
# @title find rows where 'DISTRICT' is NaN
rows_to_impute = df2[(df2['DISTRICT'].isna())]
In [45]:
# @title randomly impute districts to the identified rows
for index in rows_to_impute.index:
df2.loc[index, 'DISTRICT'] = random.choice(districts_to_impute)
In [46]:
#@title district and region dictionary
province_district = {
'KOSHI': ['TAPLEJUNG', 'SANKHUWASABHA', 'SOLUKHUMBU',
'UDAYAPUR', 'PANCHTHAR', 'ILAM', 'TERHATHUM',
'DHANKUTA', 'BHOJPUR', 'KHOTANG',
'OKHALDHUNGA', 'JHAPA', 'MORANG', 'SUNSARI'],
'MADHESH': ['MAHOTTARI', 'RAUTAHAT', 'DHANUSHA', 'SIRAHA',
'BARA', 'SARLAHI', 'PARSA', 'SAPTARI'],
'BAGMATI': ['DOLKHA', 'SINDHUPALCHOWK', 'RASUWA',
'MAKWANPUR', 'BHAKTAPUR', 'LALITPUR',
'KATHMANDU', 'NUWAKOT','RAMECHHAP', 'KAVRE',
'DHADING', 'SINDHULI', 'CHITWAN'],
'GANDAKI': ['MANANG', 'MUSTANG', 'PARBAT', 'SYANGJA','TANAHU',
'LAMJUNG', 'BAGLUNG', 'KASKI','MYAGDI', 'GORKHA', 'NAWALPARASI'],
'LUMBINI': ['PALPA', 'ARGHAKHACHI', 'RUKUM', 'GULMI', 'PYUTHAN',
'ROLPA', 'RUPANDEHI', 'KAPILBASTU','DANG', 'BANKE', 'BARDIYA'],
'KARNALI': ['DOLPA', 'MUGU', 'HUMLA', 'JUMLA', 'SALYAN',
'JAJARKOT', 'DAILEKH', 'SURKHET', 'KALIKOT'],
'SUDUR-PASCHIM': ['BAJURA', 'BAJHANG', 'DARCHULA',
'ACHHAM', 'DOTI', 'BAITADI', 'KAILALI', 'KANCHANPUR']
}
In [47]:
#@title apply function to map district to province
def map_district_to_province(district):
for province, districts in province_district.items():
if district in districts:
return province
return None
df2['PROVINCE'] = df2['DISTRICT'].apply(map_district_to_province)
In [48]:
#@title list null
print(df2[df2['PROVINCE'].isnull()]['DISTRICT'].unique().tolist())
[]
In [49]:
#@title check for impute
display(df2.query('PROVINCE == "KARNALI"'))
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | SPRINGHILLHOTEL | HUMLA | 50000000 | 47000000 | 3000000 | HOTEL22BEDSRESTAURANT40SEATS | 29 | TOURISM | SMALL | 60 | Local - 0%, Foreign - 100% | 8 | 2076 | KARNALI |
| 249 | HIMALAYANRENEWABLEOILINDUSTRY | DOLPA | 250000000 | 224492402 | 25507598 | PETROL-1485KLDIESEL-1485KLLUBRICANTS(BYPRODUCT... | 42 | MANUFACTURING | MEDIUM | 2000 | Local - 20%, Foreign - 80% | 3 | 2077 | KARNALI |
| 250 | NEBULAENERGY | HUMLA | 150000000 | 105000000 | 45000000 | ELECTRICVEHICLESASSEMBLING300NOS. | 67 | MANUFACTURING | SMALL | 500 | Local - 100%, Foreign - 0% | 3 | 2077 | KARNALI |
| 256 | PRISTINENEPALTERMINALS | KALIKOT | 80000000 | 64000000 | 16000000 | CONTAINERS14550MTBULK&BREAKBULK9050MT | 74 | SERVICE | SMALL | 100 | Local - 36%, Foreign - 64% | 4 | 2077 | KARNALI |
| 285 | SAMADHIVILLAGE | SALYAN | 50000000 | 37500000 | 12500000 | HOTEL20BEDSRESTAURANT100SEATS | 17 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 6 | 2077 | KARNALI |
| 309 | SETIKHOLAHYDROPWER | KALIKOT | 5005648000 | 4945816000 | 59832000 | HYDROELECTRICITY22M.W. | 36 | ENERGY | LARGE | 100 | Local - 100%, Foreign - 0% | 6 | 2077 | KARNALI |
| 342 | PATHIVARAMATAFERTILIZERINDUSTRIES | JAJARKOT | 219800000 | 162400000 | 57400000 | CHEMICALFERTILIZER3000M.T. | 45 | MANUFACTURING | MEDIUM | 900 | Local - 100%, Foreign - 0% | 9 | 2077 | KARNALI |
| 345 | SWARARETREAT | MUGU | 53000000 | 41500000 | 11500000 | HOTEL40BEDSRESTAURANT60SEATS | 38 | TOURISM | SMALL | 50 | Local - 5.67%, Foreign - 94.33% | 9 | 2077 | KARNALI |
| 351 | BOLBOMFEEDINDUSTRIES | KALIKOT | 340000000 | 213000000 | 127000000 | ANIMALFEED(PELLET/DISC)73000M.T. | 55 | AGRO AND FORESTRY | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 9 | 2077 | KARNALI |
| 432 | MANDAKINIHYDROPOWERCOMPANYLIMITED-1 | JAJARKOT | 569981000 | 565307297 | 4673703 | HYDROELECTRICITY2.9M.W. | 40 | ENERGY | LARGE | 20 | Local - 100%, Foreign - 0% | 1 | 2078 | KARNALI |
| 434 | ADIRATEXTILEINDUSTRIES | KALIKOT | 200000000 | 137100000 | 62900000 | VARIOUSFABRICS4200000SQ.M | 59 | MANUFACTURING | SMALL | 800 | Local - 100%, Foreign - 0% | 1 | 2078 | KARNALI |
| 435 | ADIRAFLOORS | JAJARKOT | 150000000 | 114000000 | 36000000 | PVCFLO0RINGSHEET1400MT. | 46 | MANUFACTURING | SMALL | 800 | Local - 100%, Foreign - 0% | 1 | 2078 | KARNALI |
| 492 | HIMALI HYDROFUND | KALIKOT | 1900000000 | 1850000000 | 50000000 | HYDROELECTRICITY$MW | 31 | ENERGY | LARGE | 25 | Local - 100%, Foreign - 0% | 4 | 2078 | KARNALI |
| 494 | SHIVAMVAGOILLTD | JAJARKOT | 750000000 | 400000000 | 350000000 | VANASPATIGHEEANDREFINED0IL(SOYABEANSUNFLOWERRB... | 72 | MANUFACTURING | MEDIUM | 1500 | Local - 100%, Foreign - 0% | 5 | 2078 | KARNALI |
In [50]:
#@title make a copy
df3 = df2.copy(deep=True)
In [51]:
#@title save to encode
df3.to_csv('2_features.csv', index=False)
encoding and outlier¶
In [52]:
#@title apply frequency encoding to category
district_counts = df3['CATEGORY'].value_counts(normalize=True)
df3['CATEGORY_FREQ'] = df3['CATEGORY'].map(district_counts)
display(df3[['CATEGORY','CATEGORY_FREQ']].value_counts())
CATEGORY CATEGORY_FREQ MANUFACTURING 0.310 155 TOURISM 0.258 129 SERVICE 0.166 83 ENERGY 0.144 72 AGRO AND FORESTRY 0.066 33 INFORMATION TECHNOLOGY 0.050 25 INFRASTRUCTURE 0.004 2 MINERAL 0.002 1 Name: count, dtype: int64
In [53]:
#@title reduceing district to province
display(df3['PROVINCE'].value_counts())
PROVINCE BAGMATI 267 GANDAKI 67 KOSHI 63 MADHESH 41 LUMBINI 38 KARNALI 14 SUDUR-PASCHIM 10 Name: count, dtype: int64
In [54]:
#@title import label encoding for scale
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df3['SCALE_encode'] = le.fit_transform(df3['SCALE'])
df3['SCALE_encode'] = df3['SCALE_encode'] + 1
display(df3[['SCALE','SCALE_encode']].value_counts())
SCALE SCALE_encode SMALL 3 216 MEDIUM 2 183 LARGE 1 101 Name: count, dtype: int64
In [55]:
#@title import one hot encoding for province
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
ohe.fit(df3[['PROVINCE']])
encoded_data = ohe.transform(df3[['PROVINCE']])
feature_names = ohe.get_feature_names_out(['PROVINCE'])
for i, feature_name in enumerate(feature_names):
df3[feature_name] = encoded_data[:, i]
In [56]:
#@title display encoded dataframe
display(df3)
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | HUALICONSTRUCTIONANDENGINEERING | BHAKTAPUR | 150000000 | 87000000 | 63000000 | VARIOUSKINDSOFCONSTRUCTIONWORKS(CONSTRUCTIONRE... | 88 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 4 | 2076 | BAGMATI | 0.166 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | CHISANGHYDRO | MORANG | 304505000 | 296587693 | 7917307 | Hydroelectricproduction1.8MW | 31 | ENERGY | LARGE | 30 | Local - 100%, Foreign - 0% | 4 | 2076 | KOSHI | 0.144 | 1 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 2 | MOKSHAINTERNATIONALCARGO | KATHMANDU | 50000000 | 46000000 | 4000000 | INTERNATIONALCARGOHANDLING12000MT | 50 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.166 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | TENGFEICONSTRUCTIONCOMPANY | KATHMANDU | 300000000 | 227000000 | 73000000 | CONSTRUCTIONWORKSOFVARIOUSTYPE1000000000L.S | 375 | SERVICE | MEDIUM | 100 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.166 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | S.W.SOFTWARE | LALITPUR | 250000000 | 227000000 | 23000000 | SOFTWAREDEVELOPMENT350PACKAGE | 83 | INFORMATION TECHNOLOGY | MEDIUM | 25 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.050 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | AGRIVASTUCOLDST0RAGE | KAPILBASTU | 234192207 | 160104320 | 74087887 | COLDST0RAGE1800MT.PRODUCTIONPROCESSINGAND\nSTO... | 27 | AGRO AND FORESTRY | MEDIUM | 300 | Local - 100%, Foreign - 0% | 5 | 2078 | LUMBINI | 0.066 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 496 | GHORAHICEMENTINDUSTRYLIMITED(CEMENTPACKEGING) | BANKE | 210338607 | 167189200 | 43149407 | CEMENT120000MT. | 48 | SERVICE | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 5 | 2078 | LUMBINI | 0.166 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 497 | S.S.PRODUCTS | SARLAHI | 2135500 | 885500 | 1250000 | PANMASALA(PLAINJARDA)14250KG. | 22 | MANUFACTURING | SMALL | 15 | Local - 100%, Foreign - 0% | 5 | 2078 | MADHESH | 0.310 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 498 | SHIVASHAKTIOILANDFATS(OIL) | BARA | 500000000 | 300000000 | 200000000 | REFINEDSOYABEANOIL30000MT.REFINEDSUNFLOWEROIL3... | 60 | MANUFACTURING | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 5 | 2078 | MADHESH | 0.310 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 499 | SHERPAOUTDOORSPORTSGOODSINDUSTRIES | KATHMANDU | 170000000 | 150000000 | 20000000 | READYMADEGARMENT&TREKKINGGOODSSUCHASSLEEPINGBA... | 200 | MANUFACTURING | SMALL | 300 | Local - 100%, Foreign - 0% | 5 | 2078 | BAGMATI | 0.310 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
500 rows × 23 columns
In [57]:
#@title check for any null value after mapping
display(df3[df3['YEAR'].isnull()])
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM |
|---|
In [58]:
#@title info after encode
df3.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 500 entries, 0 to 499 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 INDUSTRY NAME 500 non-null object 1 DISTRICT 500 non-null object 2 TOTAL CAPITAL 500 non-null int64 3 FIXED CAPITAL 500 non-null int64 4 WORKING CAPITAL 500 non-null int64 5 PRODUCT AND ANNUAL CAPACITY 500 non-null object 6 EMPLOYMENT 500 non-null int64 7 CATEGORY 500 non-null object 8 SCALE 500 non-null object 9 POWER 500 non-null int64 10 % OF INVESTMENT 500 non-null object 11 MONTH 500 non-null int64 12 YEAR 500 non-null int64 13 PROVINCE 500 non-null object 14 CATEGORY_FREQ 500 non-null float64 15 SCALE_encode 500 non-null int64 16 PROVINCE_BAGMATI 500 non-null float64 17 PROVINCE_GANDAKI 500 non-null float64 18 PROVINCE_KARNALI 500 non-null float64 19 PROVINCE_KOSHI 500 non-null float64 20 PROVINCE_LUMBINI 500 non-null float64 21 PROVINCE_MADHESH 500 non-null float64 22 PROVINCE_SUDUR-PASCHIM 500 non-null float64 dtypes: float64(8), int64(8), object(7) memory usage: 90.0+ KB
In [59]:
#@title save for visualization
df3.to_csv('3_encode.csv', index=False)
In [60]:
#@title copy for model
df4=df3.copy(deep=True)
model¶
In [61]:
#@title import scipy
from scipy.stats import iqr
In [62]:
#@title apply interquartile range with scipy in total capital. fixed capital and working capital
# calculate IQR for specified columns
for col in ['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL']:
q1 = df4[col].quantile(0.25)
q3 = df4[col].quantile(0.75)
iqr_val = iqr(df4[col])
print(f"IQR for {col}: {iqr_val}\n")
# filtering out outliers
upper_bound = q3 + 1.5 * iqr_val
lower_bound = q1 - 1.5 * iqr_val
df4 = df4[(df4[col] >= lower_bound) & (df4[col] <= upper_bound)]
IQR for TOTAL CAPITAL: 257600000.0 IQR for FIXED CAPITAL: 135000000.0 IQR for WORKING CAPITAL: 48200000.0
In [63]:
#@ title import plotly
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [64]:
#@title create box plots before and after IQR treatment
fig_before = px.box(df2, y=['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL'],
title='Box Plots Before IQR Treatment')
fig_before.show()
fig_after = px.box(df4, y=['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL'],
title='Box Plots After IQR Treatment')
fig_after.show()
In [65]:
#@title apply standard scaler
from sklearn.preprocessing import StandardScaler
# intitailize
scaler = StandardScaler()
# columns to standardize
columns_to_standardize = ['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL']
# standardized columns into new columns.
for col in columns_to_standardize:
# fit scaler
scaler.fit(df4[[col]])
# new column with the standardized values
df4[col + '_standardized'] = scaler.transform(df4[[col]])
In [66]:
#@title split investment
def split_investment(investment_str):
local_pct = 0
foreign_pct = 0
try:
parts = investment_str.split(',')
for part in parts:
if 'Local' in part:
local_pct = float(part.split('-')[1].replace('%', '').strip())
elif 'Foreign' in part:
foreign_pct = float(part.split('-')[1].replace('%', '').strip())
except:
print(f"Error processing: {investment_str}")
return local_pct, foreign_pct
return local_pct, foreign_pct
df4[['Local_Investment', 'Foreign_Investment']] = df2['% OF INVESTMENT'].apply(lambda x: pd.Series(split_investment(x)))
In [67]:
#@title display after split
display(df4[['% OF INVESTMENT', 'Local_Investment', 'Foreign_Investment']].sample(5))
| % OF INVESTMENT | Local_Investment | Foreign_Investment | |
|---|---|---|---|
| 459 | Local - 100%, Foreign - 0% | 100.0 | 0.0 |
| 264 | Local - 100%, Foreign - 0% | 100.0 | 0.0 |
| 158 | Local - 0%, Foreign - 100% | 0.0 | 100.0 |
| 51 | Local - 0%, Foreign - 100% | 0.0 | 100.0 |
| 73 | Local - 0%, Foreign - 100% | 0.0 | 100.0 |
In [68]:
#@title unique values for Local and Foreign Investment
print("Unique Local Investment values:")
display(df4['Local_Investment'].value_counts())
print("\nUnique Foreign Investment values:")
display(df4['Foreign_Investment'].value_counts())
Unique Local Investment values:
Local_Investment 0.000 190 100.000 153 40.000 4 51.000 3 20.000 3 6.250 2 15.000 2 36.000 1 34.221 1 50.000 1 5.670 1 9.910 1 10.000 1 15.240 1 13.040 1 49.000 1 16.667 1 20.290 1 66.420 1 6.000 1 Name: count, dtype: int64
Unique Foreign Investment values:
Foreign_Investment 100.000 189 0.000 155 60.000 4 80.000 3 49.000 3 85.000 2 93.750 2 83.333 1 51.000 1 79.710 1 33.580 1 84.760 1 86.960 1 64.000 1 90.000 1 94.330 1 50.000 1 65.779 1 94.000 1 Name: count, dtype: int64
In [69]:
#@title view list of columns
columns = df4.columns.tolist()
print("Columns:", columns)
Columns: ['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT', 'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR', 'PROVINCE', 'CATEGORY_FREQ', 'SCALE_encode', 'PROVINCE_BAGMATI', 'PROVINCE_GANDAKI', 'PROVINCE_KARNALI', 'PROVINCE_KOSHI', 'PROVINCE_LUMBINI', 'PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM', 'TOTAL CAPITAL_standardized', 'FIXED CAPITAL_standardized', 'WORKING CAPITAL_standardized', 'Local_Investment', 'Foreign_Investment']
In [70]:
#@title correlation matrix using only numerical columns
correlation_matrix = df4.select_dtypes(include=['number']).corr()
# a heatmap of the correlation matrix
fig = px.imshow(correlation_matrix,
labels=dict(x="Features", y="Features", color="Correlation"),
x=correlation_matrix.columns,
y=correlation_matrix.columns,
color_continuous_scale='RdBu',
zmin=-1, zmax=1,
title='Correlation Matrix of Numerical Features')
fig.show()
In [71]:
# @title features and target
features = ['TOTAL CAPITAL', 'FIXED CAPITAL', 'WORKING CAPITAL',
'POWER', 'SCALE_encode',
'PROVINCE_BAGMATI', 'PROVINCE_GANDAKI', 'PROVINCE_KOSHI',
'PROVINCE_LUMBINI','PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM',
'CATEGORY_FREQ','Local_Investment', 'Foreign_Investment']
target = 'CATEGORY'
In [72]:
X = df4[features]
y = df4[target]
In [73]:
#@title import and train test splot
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [74]:
#@title metrics function accuracy and classification
from sklearn.metrics import accuracy_score, classification_report
# Function to train and evaluate a model
def evaluate_model(model, X_train, X_test, y_train, y_test):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)
print(f"Model: {model.__class__.__name__}")
print(f"Accuracy: {accuracy:.4f}")
print(f"Classification Report:\n{report}\n")
return accuracy, report
In [75]:
# @title Import models
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
In [76]:
#@title list of models to evaluate
models = [
LogisticRegression(max_iter=1000, random_state=42),
RandomForestClassifier(random_state=42),
]
In [77]:
# @title evaluate each model
results = {} # Store results for comparison
for model in models:
accuracy, report = evaluate_model(model, X_train, X_test, y_train, y_test)
results[model.__class__.__name__] = {'accuracy': accuracy, 'report': report}
Model: LogisticRegression
Accuracy: 0.6216
Classification Report:
precision recall f1-score support
AGRO AND FORESTRY 0.00 0.00 0.00 7
ENERGY 0.00 0.00 0.00 1
INFORMATION TECHNOLOGY 0.00 0.00 0.00 2
INFRASTRUCTURE 0.00 0.00 0.00 1
MANUFACTURING 0.62 0.89 0.73 18
SERVICE 0.70 0.41 0.52 17
TOURISM 0.61 0.82 0.70 28
accuracy 0.62 74
macro avg 0.27 0.30 0.28 74
weighted avg 0.54 0.62 0.56 74
Model: RandomForestClassifier
Accuracy: 0.9595
Classification Report:
precision recall f1-score support
AGRO AND FORESTRY 0.83 0.71 0.77 7
ENERGY 0.50 1.00 0.67 1
INFORMATION TECHNOLOGY 1.00 1.00 1.00 2
INFRASTRUCTURE 0.00 0.00 0.00 1
MANUFACTURING 1.00 1.00 1.00 18
SERVICE 1.00 1.00 1.00 17
TOURISM 0.97 1.00 0.98 28
accuracy 0.96 74
macro avg 0.76 0.82 0.77 74
weighted avg 0.95 0.96 0.95 74
In [78]:
#@title save for visualization
df3.to_csv('4_visuals.csv', index=False)
visualization¶
In [79]:
#@title for visuals
df0=pd.read_csv('4_visuals.csv')
In [80]:
#@title 'Scale' vs 'Employment', by 'District'
fig = px.bar(df0, x='EMPLOYMENT', y='SCALE', color='PROVINCE',
title="Employment vs Scale by Province",
labels={'Province': 'Province Name'})
fig.show()
In [81]:
#@title 'Scale' vs 'Power', by 'District'
fig = px.bar(df0, x='POWER', y='SCALE', color='PROVINCE',
title="Power vs Scale by Province",
labels={'Province': 'Province Name'})
fig.show()
In [82]:
#@title Scatter plot of TOTAL CAPITAL vs. EMPLOYMENT
fig = px.scatter(df0, x="TOTAL CAPITAL", y="FIXED CAPITAL",
title="Total Capital vs. Employment",
hover_data=['CATEGORY'])
fig.show()
In [83]:
print(df4.columns)
print(df4.shape)
Index(['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL',
'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT',
'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR',
'PROVINCE', 'CATEGORY_FREQ', 'SCALE_encode', 'PROVINCE_BAGMATI',
'PROVINCE_GANDAKI', 'PROVINCE_KARNALI', 'PROVINCE_KOSHI',
'PROVINCE_LUMBINI', 'PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM',
'TOTAL CAPITAL_standardized', 'FIXED CAPITAL_standardized',
'WORKING CAPITAL_standardized', 'Local_Investment',
'Foreign_Investment'],
dtype='object')
(370, 28)
In [84]:
print(df0.columns)
print(df0.shape)
Index(['INDUSTRY NAME', 'DISTRICT', 'TOTAL CAPITAL', 'FIXED CAPITAL',
'WORKING CAPITAL', 'PRODUCT AND ANNUAL CAPACITY', 'EMPLOYMENT',
'CATEGORY', 'SCALE', 'POWER', '% OF INVESTMENT', 'MONTH', 'YEAR',
'PROVINCE', 'CATEGORY_FREQ', 'SCALE_encode', 'PROVINCE_BAGMATI',
'PROVINCE_GANDAKI', 'PROVINCE_KARNALI', 'PROVINCE_KOSHI',
'PROVINCE_LUMBINI', 'PROVINCE_MADHESH', 'PROVINCE_SUDUR-PASCHIM'],
dtype='object')
(500, 23)
In [85]:
# @title Bar chart of CATEGORY counts
fig = px.histogram(df0, x="CATEGORY",
title="Distribution of Categories")
fig.show()
In [86]:
# @title Box plot of TOTAL CAPITAL by CATEGORY
fig = px.box(df0, x="CATEGORY", y="TOTAL CAPITAL",
title="Total Capital by Category")
fig.show()
In [87]:
# @title EMPLOYMENT
fig = go.Figure()
fig.add_trace(go.Scatter(x=df0.index, y=df0['EMPLOYMENT'],
mode='lines', name='EMPLOYMENT'))
fig.update_layout(title='EMPLOYMENT',
xaxis_title='Index',
yaxis_title='EMPLOYMENT')
fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=False)
fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=False)
fig.show()
In [88]:
# @title CATEGORY
fig = px.bar(df0.groupby('CATEGORY').size().reset_index(name='count'),
y='CATEGORY', x='count', orientation='h',
color='CATEGORY',
title='Distribution of Categories',
text='count') # Add the 'text' parameter
fig.update_layout(showlegend=False) # Hide legend if not needed
fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=False)
fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=False)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside') # Format and position the text
fig.show()
In [89]:
# @title SCALE
fig = px.bar(df0.groupby('SCALE').size().reset_index(name='count'),
x='SCALE', y='count',
color='SCALE',
title='Distribution of Scales',
text='count')
fig.update_layout(
xaxis_title='Scale',
yaxis_title='Count',
plot_bgcolor='rgba(0,0,0,0)',
xaxis={'showline': True, 'linewidth': 1, 'linecolor': 'black'},
yaxis={'showline': True, 'linewidth': 1, 'linecolor': 'black'},
)
fig.update_traces(marker_line_width=1, marker_line_color='black',
texttemplate='%{text:.2s}', textposition='outside')
fig.show()
In [90]:
# @title Stacked Bar Chart with Range Slider (Interval)
fig = px.bar(df4, x=df4.index, y=['TOTAL CAPITAL','FIXED CAPITAL', 'WORKING CAPITAL'],
title='Total, Fixed, and Working Capital',
labels={'value': 'Capital', 'variable': 'Capital Type'})
fig.update_layout(
xaxis=dict(
rangeslider=dict(visible=True),
type="linear",
range=[00,100]
)
)
fig.show()
In [91]:
#@title Employment by Scale, Category, Province and District in sun burst chart
fig = px.sunburst(df0, path=['SCALE', 'CATEGORY','PROVINCE','DISTRICT'], values='EMPLOYMENT',
title='Employment by Scale, Category, Province and District', height=800, width=1000)
fig.show()
In [92]:
# @title employment by district
df_employment = df0.groupby('CATEGORY')['EMPLOYMENT'].sum().sort_values(ascending=False)
fig = px.pie(df_employment, values='EMPLOYMENT', names=df_employment.index,
title='Employment by Category')
fig.show()
In [93]:
# @title stacked area chart
fig = px.bar(df0, x="CATEGORY", y="POWER",
title="Power Consumption by Category")
fig.show()
In [94]:
#@title power consumption by category
fig = px.pie(df0, values='POWER', names='CATEGORY', hole=0.4,
title='Power Consumption by Category')
fig.update_traces(textposition='outside', textinfo='percent+label')
fig.show()
In [95]:
#@title nightingale the values for each category
category_values = df4.groupby('CATEGORY')['TOTAL CAPITAL_standardized'].sum()
# Create the Nightingale rose chart
fig = go.Figure(go.Barpolar(
r=category_values,
theta=category_values.index,
width=[0.8] * len(category_values),
marker_color=px.colors.qualitative.Plotly,
))
fig.update_layout(
title="Nightingale Rose Chart of Total Capital by Category",
polar=dict(
radialaxis=dict(
visible=True,
),
),
showlegend=True
)
fig.show()
In [96]:
# @title Treemap Visualization
fig = px.treemap(df0, path=[ 'SCALE','PROVINCE', 'CATEGORY'], values='EMPLOYMENT',
color='TOTAL CAPITAL', hover_data=['POWER'],
color_continuous_scale='RdBu',
title="Treemap of Employment by Category, District, and Scale")
fig.show()
In [97]:
#@title treemap total capital by category and scale
fig = px.treemap(df0, path=['SCALE','CATEGORY'], values='TOTAL CAPITAL',
title='Treemap of Total Capital by Category and Scale')
fig.show()
In [98]:
import plotly.graph_objects as go
# Initialize lists to store link data
source = []
target = []
value = []
# Iterate through dataframe and create links based on values
for index, row in df0.iterrows():
source_index = list(df0['CATEGORY'].unique()).index(row['CATEGORY'])
target_index = len(df0['CATEGORY'].unique()) + list(df0['SCALE'].unique()).index(row['SCALE'])
source.append(source_index)
target.append(target_index)
value.append(row['TOTAL CAPITAL'])
target_index_2 = len(df0['CATEGORY'].unique()) + len(df0['SCALE'].unique()) + list(df0['PROVINCE'].unique()).index(row['PROVINCE'])
source.append(target_index)
target.append(target_index_2)
value.append(row['EMPLOYMENT'])
# Create the Sankey diagram with the pre-populated link data
fig = go.Figure(data=[go.Sankey(
node=dict(
pad=15,
thickness=20,
line=dict(color="black", width=0.5),
label=df0['CATEGORY'].unique().tolist() + df0['SCALE'].unique().tolist() + df0['PROVINCE'].unique().tolist(), # Combine all categories
color="blue"
),
link=dict(
source=source, # Assign the pre-populated source list
target=target, # Assign the pre-populated target list
value=value # Assign the pre-populated value list
))])
fig.update_layout(title_text="Network Spider Web Diagram", font_size=10)
fig.show()
In [99]:
#@title network
import plotly.graph_objects as go
import networkx as nx
# Create a graph from the DataFrame (df2)
graph = nx.from_pandas_edgelist(df0, source='DISTRICT', target='CATEGORY', edge_attr=True)
# Create a Plotly graph object
fig = go.Figure(data=[go.Scatter(
x=[pos[0] for pos in nx.spring_layout(graph).values()],
y=[pos[1] for pos in nx.spring_layout(graph).values()],
mode='markers+text',
text=list(graph.nodes),
marker=dict(
size=10,
color='blue'
)
)])
# Customize the layout
fig.update_layout(
title="District-Category Network Graph",
width=1000, # Adjust width
height=800, # Adjust height
showlegend=False,
)
fig.show()
In [100]:
# @title Funnel Chart
employment_by_category = df0.groupby('PROVINCE')['EMPLOYMENT'].sum().reset_index()
fig = go.Figure(go.Funnel(
y = employment_by_category['PROVINCE'],
x = employment_by_category['EMPLOYMENT'],
textposition = "inside",
textinfo = "value+percent initial",
))
fig.update_layout(title="Funnel Chart of Employment by Proince")
fig.show()
In [101]:
# @title Violin Plots for Employment by District
fig = px.violin(df0, x='PROVINCE', y='EMPLOYMENT', color='CATEGORY', box=True, points='all',
title='Distribution of Employment by District and Category')
fig.show()
In [102]:
# @title Box Plots for Capital by Category
fig = px.box(df0, x='PROVINCE', y='CATEGORY', color='SCALE',
title='Distribution of Total Capital by Category and Scale')
fig.show()
In [103]:
# @title Parallel Categories Plot with more dimensions
fig = px.parallel_categories(df0, dimensions=['CATEGORY', 'PROVINCE', 'SCALE'], color="EMPLOYMENT",
title='Parallel Categories Plot of Key Metrics')
fig.show()
In [104]:
# @title sepearte dataframes based on different province , categories and scale
provinces = df0['PROVINCE'].unique()
categories = df0['CATEGORY'].unique()
scales = df0['SCALE'].unique()
separated_dfs = {}
for province in provinces:
for category in categories:
for scale in scales:
key = f"{province}_{category}_{scale}"
separated_dfs[key] = df0[
(df0['PROVINCE'] == province) &
(df0['CATEGORY'] == category) &
(df0['SCALE'] == scale)
].copy()
# Example usage to show the first 5 rows of each DataFrame:
for key, df in separated_dfs.items():
if not df.empty:
print(f"\nDataFrame for {key}:")
display(df.head(2))
else:
print(f"\nDataFrame for {key}: (Empty)")
DataFrame for BAGMATI_SERVICE_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | HUALICONSTRUCTIONANDENGINEERING | BHAKTAPUR | 150000000 | 87000000 | 63000000 | VARIOUSKINDSOFCONSTRUCTIONWORKS(CONSTRUCTIONRE... | 88 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 4 | 2076 | BAGMATI | 0.166 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | MOKSHAINTERNATIONALCARGO | KATHMANDU | 50000000 | 46000000 | 4000000 | INTERNATIONALCARGOHANDLING12000MT | 50 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.166 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_SERVICE_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 90 | CRCCFOURTEENNEPAL | SINDHUPALCHOWK | 1000000000 | 500000000 | 500000000 | CONSTRUCTIONWORKS(ONVARIOUSSECTORSLIKEROADBRID... | 330 | SERVICE | LARGE | 25 | Local - 0%, Foreign - 100% | 7 | 2076 | BAGMATI | 0.166 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 222 | PEOPLESDENTALCOLLEGE&HOSPITAL | KATHMANDU | 616000000 | 586000000 | 30000000 | FOURBEDSWARDS80BED | 181 | SERVICE | LARGE | 400 | Local - 100%, Foreign - 0% | 12 | 2076 | BAGMATI | 0.166 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_SERVICE_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | TENGFEICONSTRUCTIONCOMPANY | KATHMANDU | 300000000 | 227000000 | 73000000 | CONSTRUCTIONWORKSOFVARIOUSTYPE1000000000L.S | 375 | SERVICE | MEDIUM | 100 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.166 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6 | A.B.C.INTERNATIONALCARGO | KATHMANDU | 150000000 | 141000000 | 9000000 | INTERNATIONALCARGOLANDLING30000MT | 64 | SERVICE | MEDIUM | 15 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.166 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_ENERGY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 264 | DIPJOYOTIHYDROPOWER | DOLKHA | 117500000 | 102500000 | 15000000 | HYDROELECTRICITY550K.W. | 10 | ENERGY | SMALL | 35 | Local - 100%, Foreign - 0% | 4 | 2077 | BAGMATI | 0.144 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_ENERGY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 63 | MILAREPAENERGY | SINDHUPALCHOWK | 4040000000 | 3997000000 | 43000000 | 23.6M.W. | 30 | ENERGY | LARGE | 100 | Local - 100%, Foreign - 0% | 7 | 2076 | BAGMATI | 0.144 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 88 | UPPERBALEPHIHYDROPOWERLIMITED | SINDHUPALCHOWK | 5800000000 | 5732319033 | 67680967 | 46M.W. | 70 | ENERGY | LARGE | 50 | Local - 100%, Foreign - 0% | 7 | 2076 | BAGMATI | 0.144 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_ENERGY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 239 | MELAMCHIHYDRO | SINDHUPALCHOWK | 197299619 | 188121380 | 9178239 | HYDROPOWER998KW. | 10 | ENERGY | MEDIUM | 50 | Local - 100%, Foreign - 0% | 3 | 2077 | BAGMATI | 0.144 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 291 | PATHIBHARAHYDROPOWER | DOLKHA | 284620030 | 269057100 | 15562930 | HYDROELECTRICITY1.1M.W. | 28 | ENERGY | MEDIUM | 25 | Local - 100%, Foreign - 0% | 6 | 2077 | BAGMATI | 0.144 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_INFORMATION TECHNOLOGY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 21 | ZEGALNEPAL | LALITPUR | 5000000 | 3000000 | 2000000 | SOFTWAREDEVELOPMENT100PACKAGES | 13 | INFORMATION TECHNOLOGY | SMALL | 12 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.05 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 87 | BEY0NDIDNEPAL | KATHMANDU | 5000000 | 3000000 | 2000000 | SOFTWAREDEVELOPMENT100PACKAGES | 13 | INFORMATION TECHNOLOGY | SMALL | 12 | Local - 0%, Foreign - 100% | 7 | 2076 | BAGMATI | 0.05 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_INFORMATION TECHNOLOGY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | VIANETCOMMUNICATIONSLTD | LALITPUR | 840000000 | 785600000 | 54400000 | HOME53742PERSONSMALLOFFICE/HOMEOFFICE3634\nPER... | 510 | INFORMATION TECHNOLOGY | LARGE | 100 | Local - 100%, Foreign - 0% | 8 | 2076 | BAGMATI | 0.05 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_INFORMATION TECHNOLOGY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | S.W.SOFTWARE | LALITPUR | 250000000 | 227000000 | 23000000 | SOFTWAREDEVELOPMENT350PACKAGE | 83 | INFORMATION TECHNOLOGY | MEDIUM | 25 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.05 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 79 | D.D.L.SOFTWARECOMPANY | LALITPUR | 250000000 | 240000000 | 10000000 | SOFTWAREDEVELOPMENT—450PACKAGES | 120 | INFORMATION TECHNOLOGY | MEDIUM | 30 | Local - 0%, Foreign - 100% | 7 | 2076 | BAGMATI | 0.05 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_TOURISM_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | HONGYUNVEGETERIANRESTAURANT | LALITPUR | 100000000 | 92000000 | 8000000 | HOTEL40BEDSRESTAURANT80SEATS | 35 | TOURISM | SMALL | 30 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.258 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 20 | MALAHOTELANDRESTAURANT | KATHMANDU | 50000000 | 47000000 | 3000000 | HOTEL28BEDSRESTAURANT50SEATS | 30 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.258 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_TOURISM_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | BALAJIHOLDINGSANDHOTEL | KATHMANDU | 848000000 | 833000000 | 15000000 | HOTEL98BEDSRESTAURANT200SEATS | 46 | TOURISM | LARGE | 500 | Local - 100%, Foreign - 0% | 5 | 2076 | BAGMATI | 0.258 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 56 | MARGARETHOTELANDRESTAURANT | KATHMANDU | 300000000 | 290000000 | 10000000 | HOTEL48BEDSRESTAURANT60SEATS | 45 | TOURISM | LARGE | 150 | Local - 0%, Foreign - 100% | 6 | 2076 | BAGMATI | 0.258 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_TOURISM_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | QINGYUNHOTEL | KATHMANDU | 250000000 | 245000000 | 5000000 | HOTEL48BEDS | 45 | TOURISM | MEDIUM | 50 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.258 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 11 | HOTELLIFANG | KATHMANDU | 150000000 | 145000000 | 5000000 | HOTEL40BEDSRESTAURANT50SEATS | 40 | TOURISM | MEDIUM | 75 | Local - 0%, Foreign - 100% | 5 | 2076 | BAGMATI | 0.258 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17 | NEPALSANYOUDOORSANDWINDOWSCOMPANY | BHAKTAPUR | 160000000 | 99210000 | 60790000 | ALUMINIUMDOORSANDWINDOWS189000SQ.FT. | 58 | MANUFACTURING | SMALL | 100 | Local - 6.25%, Foreign - 93.75% | 5 | 2076 | BAGMATI | 0.31 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 36 | SARALURJANEPAL | KATHMANDU | 40000000 | 23400000 | 16600000 | SOLARPVSYSTEMFORHOMEAPPLIANCE1000SEATSSOLARPVS... | 58 | MANUFACTURING | SMALL | 40 | Local - 66.42%, Foreign - 33.58% | 6 | 2076 | BAGMATI | 0.31 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_MANUFACTURING_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | DERRENPHARMACEUTICALS | LALITPUR | 1301973000 | 1155534000 | 146439000 | TABLETS250MILLIONCAPSULES8MILLION\n0NIMENTS35M... | 184 | MANUFACTURING | LARGE | 500 | Local - 100%, Foreign - 0% | 7 | 2076 | BAGMATI | 0.31 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 484 | SARASBEVERAGE | DHADING | 2000000000 | 1200000000 | 800000000 | JUICE10000KLCARBONATEDSOFTDRINKS20000KLENERGYD... | 180 | MANUFACTURING | LARGE | 1000 | Local - 100%, Foreign - 0% | 4 | 2078 | BAGMATI | 0.31 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | DREAMPAINTSNEPAL | MAKWANPUR | 255720000 | 135720000 | 120000000 | PAINT8400KLPUTTY670M.T. | 92 | MANUFACTURING | MEDIUM | 500 | Local - 100%, Foreign - 0% | 5 | 2076 | BAGMATI | 0.31 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 19 | LIFEFOODANDBEVERAGE | CHITWAN | 249500000 | 171500000 | 78000000 | FRUITANDVEGETABLEJUICE25000KLENERGYDRINK(NONAL... | 142 | MANUFACTURING | MEDIUM | 625 | Local - 100%, Foreign - 0% | 5 | 2076 | BAGMATI | 0.31 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_AGRO AND FORESTRY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 42 | JYAMRUNGAGRICULTUREFARM | DHADING | 5000000 | 4050000 | 950000 | VEGETABLES78MTFRUITS14MT | 20 | AGRO AND FORESTRY | SMALL | 15 | Local - 0%, Foreign - 100% | 6 | 2076 | BAGMATI | 0.066 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 134 | LOTUSCOLDSTORAGE | KATHMANDU | 50000000 | 42000000 | 8000000 | COLDSTORAGEOFVAGETABLESANDFRUITS-6000MT | 35 | AGRO AND FORESTRY | SMALL | 200 | Local - 0%, Foreign - 100% | 8 | 2076 | BAGMATI | 0.066 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_AGRO AND FORESTRY_LARGE: (Empty) DataFrame for BAGMATI_AGRO AND FORESTRY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | MANIKHELJADIBUTIFARM | LALITPUR | 100000000 | 89265000 | 10735000 | HERBALPOWDER300MTESSENTIALOIL30000LITERDRIEDHE... | 58 | AGRO AND FORESTRY | MEDIUM | 200 | Local - 0%, Foreign - 100% | 8 | 2076 | BAGMATI | 0.066 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 101 | CHINESEHERBALUDHOGLTD | LALITPUR | 150000000 | 95900000 | 54100000 | HERBALPOWDER550MTESSENTIALOIL35000LITERDRIEDHE... | 114 | AGRO AND FORESTRY | MEDIUM | 400 | Local - 0%, Foreign - 100% | 8 | 2076 | BAGMATI | 0.066 | 2 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_MINERAL_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 347 | BABAMINES&MINERALS | MAKWANPUR | 123446032 | 112101500 | 11344532 | LIMESTONE480000M.T.MARBLEBLOCK150MCUBEMARBLECH... | 92 | MINERAL | SMALL | 800 | Local - 100%, Foreign - 0% | 9 | 2077 | BAGMATI | 0.002 | 3 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for BAGMATI_MINERAL_LARGE: (Empty) DataFrame for BAGMATI_MINERAL_MEDIUM: (Empty) DataFrame for BAGMATI_INFRASTRUCTURE_SMALL: (Empty) DataFrame for BAGMATI_INFRASTRUCTURE_LARGE: (Empty) DataFrame for BAGMATI_INFRASTRUCTURE_MEDIUM: (Empty) DataFrame for KOSHI_SERVICE_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 382 | ARCHIELABELPRINTERS | MORANG | 155600000 | 120600000 | 35000000 | LABELIMPRESSION(1.15MTOR75000SEATPERDAY)318M.T. | 33 | SERVICE | SMALL | 200 | Local - 100%, Foreign - 0% | 11 | 2077 | KOSHI | 0.166 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_SERVICE_LARGE: (Empty) DataFrame for KOSHI_SERVICE_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 234 | MAHARISHIVEDICINSTITUTE(1) | JHAPA | 510000000 | 490000000 | 20000000 | SANSKRITLANGUAGETRAINING3000NOS.NEPALILANGUAGE... | 75 | SERVICE | MEDIUM | 100 | Local - 0%, Foreign - 100% | 3 | 2077 | KOSHI | 0.166 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_ENERGY_SMALL: (Empty) DataFrame for KOSHI_ENERGY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CHISANGHYDRO | MORANG | 304505000 | 296587693 | 7917307 | Hydroelectricproduction1.8MW | 31 | ENERGY | LARGE | 30 | Local - 100%, Foreign - 0% | 4 | 2076 | KOSHI | 0.144 | 1 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 13 | HYDROCONNECTION | SOLUKHUMBU | 3124544230 | 3022138750 | 102405480 | Hydropower18MW. | 68 | ENERGY | LARGE | 100 | Local - 100%, Foreign - 0% | 5 | 2076 | KOSHI | 0.144 | 1 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_ENERGY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 194 | HALESIURJA | KHOTANG | 420420000 | 410559320 | 9860680 | 2.2M.W. | 17 | ENERGY | MEDIUM | 50 | Local - 100%, Foreign - 0% | 11 | 2076 | KOSHI | 0.144 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_INFORMATION TECHNOLOGY_SMALL: (Empty) DataFrame for KOSHI_INFORMATION TECHNOLOGY_LARGE: (Empty) DataFrame for KOSHI_INFORMATION TECHNOLOGY_MEDIUM: (Empty) DataFrame for KOSHI_TOURISM_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 156 | CELESTIALRESORTS | ILAM | 100000000 | 95000000 | 5000000 | HOTEL30BEDSRESTAURANT50SEATS | 35 | TOURISM | SMALL | 70 | Local - 0%, Foreign - 100% | 9 | 2076 | KOSHI | 0.258 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 238 | FATTSERNVEGRESTAURANT | SUNSARI | 50000000 | 33000000 | 17000000 | RESTAURANT40SEATS | 18 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 3 | 2077 | KOSHI | 0.258 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_TOURISM_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 453 | MOUNTEVERESTCABLECAR | SOLUKHUMBU | 660000000 | 625000000 | 35000000 | CABLECAR1120000PERSONS | 68 | TOURISM | LARGE | 2000 | Local - 100%, Foreign - 0% | 3 | 2078 | KOSHI | 0.258 | 1 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_TOURISM_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 126 | VEGASRECREATIONNEPAL | JHAPA | 250000000 | 192000000 | 58000000 | CASINOPLAYERS200000PERSONS | 355 | TOURISM | MEDIUM | 150 | Local - 100%, Foreign - 0% | 8 | 2076 | KOSHI | 0.258 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 191 | NEPALIRIKAHOTEL | MORANG | 222925000 | 207900000 | 15025000 | HOTEL48BEDSRESTAURANT100SEATS | 39 | TOURISM | MEDIUM | 100 | Local - 100%, Foreign - 0% | 11 | 2076 | KOSHI | 0.258 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | KANKAIRUBBERINDUSTRIES | JHAPA | 200000000 | 130000000 | 70000000 | RUBBERPRODUCTS(SH0ESSOLEDOORMATBELTSTRAPWASHER... | 80 | MANUFACTURING | SMALL | 300 | Local - 100%, Foreign - 0% | 11 | 2076 | KOSHI | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 204 | JAYSHREEPOLYMERS(1) | SUNSARI | 136405000 | 106405000 | 30000000 | SLIPPER1800000PAIR | 32 | MANUFACTURING | SMALL | 500 | Local - 100%, Foreign - 0% | 11 | 2076 | KOSHI | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_MANUFACTURING_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 393 | PRESIDENTBI0TECHNEPAL | SUNSARI | 1750000000 | 1253600000 | 496400000 | 0RGANICFERTILIZER3000.Z. | 30 | MANUFACTURING | LARGE | 1200 | Local - 15%, Foreign - 85% | 12 | 2077 | KOSHI | 0.31 | 1 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14 | PHARMONICSLIFESCIENCES | SUNSARI | 350000000 | 240500000 | 109500000 | TABLETS100MILLION\nCAPSULES50MILLIONOINTMENTS2... | 45 | MANUFACTURING | MEDIUM | 500 | Local - 100%, Foreign - 0% | 5 | 2076 | KOSHI | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 135 | ASIACHEM | MORANG | 245000000 | 190000000 | 55000000 | PRINTEDPACKAGINGWRAPPER2000M.T. | 80 | MANUFACTURING | MEDIUM | 500 | Local - 100%, Foreign - 0% | 8 | 2076 | KOSHI | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_AGRO AND FORESTRY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 214 | NAYABAZAARKRISHIFARM | ILAM | 135000000 | 126500000 | 8500000 | CTCTEA75M.T.ORTHODOXTEA275M.T.COFFEE20M.T. | 54 | AGRO AND FORESTRY | SMALL | 380 | Local - 100%, Foreign - 0% | 11 | 2076 | KOSHI | 0.066 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 317 | MARUTIDAIRYPRODUCT | MORANG | 150000000 | 116000000 | 34000000 | PASTEURIZEDSKIMMEDMILK2200000LITERCURD100000LI... | 27 | AGRO AND FORESTRY | SMALL | 350 | Local - 100%, Foreign - 0% | 7 | 2077 | KOSHI | 0.066 | 3 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_AGRO AND FORESTRY_LARGE: (Empty) DataFrame for KOSHI_AGRO AND FORESTRY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 312 | ARIHANTAGRIFARM&RESEARCHCENTER | SUNSARI | 220000000 | 170000000 | 50000000 | GREENVEGETABLES50M.T.FRUITS100M.T.GRAINS(RICEW... | 264 | AGRO AND FORESTRY | MEDIUM | 200 | Local - 100%, Foreign - 0% | 7 | 2077 | KOSHI | 0.066 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 418 | SUNSARIPOULTRY | SUNSARI | 200000000 | 184300000 | 15700000 | EGG(HEN)56000000NOS.CULLEDLAYERHEN(BYPRODUCT)2... | 83 | AGRO AND FORESTRY | MEDIUM | 300 | Local - 100%, Foreign - 0% | 12 | 2077 | KOSHI | 0.066 | 2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KOSHI_MINERAL_SMALL: (Empty) DataFrame for KOSHI_MINERAL_LARGE: (Empty) DataFrame for KOSHI_MINERAL_MEDIUM: (Empty) DataFrame for KOSHI_INFRASTRUCTURE_SMALL: (Empty) DataFrame for KOSHI_INFRASTRUCTURE_LARGE: (Empty) DataFrame for KOSHI_INFRASTRUCTURE_MEDIUM: (Empty) DataFrame for GANDAKI_SERVICE_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 269 | GA0YUCARGO | KASKI | 200000000 | 110000000 | 90000000 | INTERNATIONALCARGOHANDLINGSERVICE88000M.T. | 31 | SERVICE | SMALL | 40 | Local - 0%, Foreign - 100% | 4 | 2077 | GANDAKI | 0.166 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 326 | XIELIWANZHONGCONSTRUCTION | LAMJUNG | 50000000 | 15000000 | 35000000 | CONSTRUCTIONWORKS(ONVARIOUSSECTORSLIKERAILWAYR... | 100 | SERVICE | SMALL | 20 | Local - 0%, Foreign - 100% | 7 | 2077 | GANDAKI | 0.166 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_SERVICE_LARGE: (Empty) DataFrame for GANDAKI_SERVICE_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | SAIARCHANAHOSPITAL | KASKI | 160000000 | 142265450 | 17734550 | HOSPITAL25BEDS | 51 | SERVICE | MEDIUM | 200 | Local - 100%, Foreign - 0% | 5 | 2076 | GANDAKI | 0.166 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 155 | ZH0NGDINGINDUSTRIAL | BAGLUNG | 200000000 | 180000000 | 20000000 | CARRYINGOUTFEASIBILITYSTUDYOFCOPPERMINERALS1SITE | 27 | SERVICE | MEDIUM | 30 | Local - 0%, Foreign - 100% | 9 | 2076 | GANDAKI | 0.166 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_ENERGY_SMALL: (Empty) DataFrame for GANDAKI_ENERGY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 25 | MOUNTRASUWAHYDROPOWER | LAMJUNG | 3163000000 | 3072908562 | 90091438 | \\Q | 31 | ENERGY | LARGE | 100 | Local - 100%, Foreign - 0% | 5 | 2076 | GANDAKI | 0.144 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 26 | BARPAKDARAUDIHYDROPOWER | GORKHA | 1698015666 | 1650099171 | 47916495 | M10MW | 45 | ENERGY | LARGE | 200 | Local - 100%, Foreign - 0% | 5 | 2076 | GANDAKI | 0.144 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_ENERGY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 195 | SAURYAVIDHYUTPOWER | NAWALPARASI | 162650000 | 160000000 | 2650000 | SOLARELECTRICITY2M.W. | 6 | ENERGY | MEDIUM | 50 | Local - 100%, Foreign - 0% | 11 | 2076 | GANDAKI | 0.144 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 463 | HIMALAYANENGINEERINGANDENERGY | NAWALPARASI | 441264447 | 433764243 | 7500204 | HYDROELECTRICITY2M.W.ANNUALSALEABLEENERGY\n13.... | 28 | ENERGY | MEDIUM | 25 | Local - 100%, Foreign - 0% | 4 | 2078 | GANDAKI | 0.144 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_INFORMATION TECHNOLOGY_SMALL: (Empty) DataFrame for GANDAKI_INFORMATION TECHNOLOGY_LARGE: (Empty) DataFrame for GANDAKI_INFORMATION TECHNOLOGY_MEDIUM: (Empty) DataFrame for GANDAKI_TOURISM_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | INTENTNEPAL | KASKI | 50000000 | 45300000 | 4700000 | HOTEL35BEDSRESTAURANT50SEATS | 28 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 5 | 2076 | GANDAKI | 0.258 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 8 | HONGTEL | KASKI | 50000000 | 45200000 | 4800000 | HOTEL35BEDSRESTAURANT50SEATS | 28 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 5 | 2076 | GANDAKI | 0.258 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_TOURISM_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | SARANGKOTMOUTAINRES0RTANDSPA | KASKI | 500000000 | 390700944 | 109299056 | HOTEL48BEDSRESTAURANT200SEATS | 60 | TOURISM | LARGE | 200 | Local - 100%, Foreign - 0% | 5 | 2076 | GANDAKI | 0.258 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 97 | BHADRAKALIHOTELIERS | KASKI | 1250000000 | 1210000000 | 40000000 | HOTEL140BEDSRESTAURANT200SEATS | 100 | TOURISM | LARGE | 1500 | Local - 100%, Foreign - 0% | 8 | 2076 | GANDAKI | 0.258 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_TOURISM_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 53 | ENTERTRAINMENTS | KASKI | 249281796 | 226713796 | 22568000 | ENTERTRAINMENT119588PERSONS | 114 | TOURISM | MEDIUM | 1200 | Local - 100%, Foreign - 0% | 6 | 2076 | GANDAKI | 0.258 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 77 | SQUAREROOTHOTEL | KASKI | 200000000 | 192000000 | 8000000 | HOTEL48BEDSRESTAURANT80SEATS | 40 | TOURISM | MEDIUM | 75 | Local - 0%, Foreign - 100% | 7 | 2076 | GANDAKI | 0.258 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 41 | SHANTIENGINEERING | KASKI | 16500000 | 12800000 | 3700000 | METALSTORAGETANKMETALST0REGADHIK0BODYSOLARWATE... | 1 | MANUFACTURING | SMALL | 15 | Local - 20.29%, Foreign - 79.71% | 6 | 2076 | GANDAKI | 0.31 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 362 | MANANGBEVERAGES | MANANG | 93500000 | 88000000 | 5500000 | WINE125000LTR.CIDER100000LTR. | 48 | MANUFACTURING | SMALL | 500 | Local - 100%, Foreign - 0% | 10 | 2077 | GANDAKI | 0.31 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_MANUFACTURING_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 479 | SIDDHILAXMIFOODS(RICE&DAAL) | NAWALPARASI | 1130000000 | 800000000 | 330000000 | RICE33840MTDAL/PULSE(ARHARMASSCHANNAMUGNKERAUM... | 224 | MANUFACTURING | LARGE | 2000 | Local - 100%, Foreign - 0% | 4 | 2078 | GANDAKI | 0.31 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 480 | SIDDHILAXMIFOODS(OIL) | NAWALPARASI | 1730000000 | 760000000 | 970000000 | RBDPALM/PALMOELINOIL30000MT.REFINEDSOYABEANOIL... | 253 | MANUFACTURING | LARGE | 2000 | Local - 100%, Foreign - 0% | 4 | 2078 | GANDAKI | 0.31 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 116 | SIDDHILAXMITUBES | NAWALPARASI | 249300000 | 200438000 | 48862000 | MSTUBE/PIPE70000M.T.SSPIPE6000M.T.TELESCOPEPOL... | 52 | MANUFACTURING | MEDIUM | 3000 | Local - 100%, Foreign - 0% | 8 | 2076 | GANDAKI | 0.31 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 140 | SAGTANISTEELINDUSTRIES | NAWALPARASI | 172286684 | 119061660 | 53225024 | SSSINK50M.T.SSTABLE50M.T.SSEXHAUSTHO0D30M.T.SS... | 39 | MANUFACTURING | MEDIUM | 1200 | Local - 100%, Foreign - 0% | 8 | 2076 | GANDAKI | 0.31 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_AGRO AND FORESTRY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 353 | JANATAAGR0ANDFEED | NAWALPARASI | 155000000 | 107661842 | 47338158 | ANIMALFEED(PELLET/DISC)24000M.T. | 45 | AGRO AND FORESTRY | SMALL | 1000 | Local - 100%, Foreign - 0% | 9 | 2077 | GANDAKI | 0.066 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 472 | HELAINGHERBSNEPAL | NAWALPARASI | 10000000 | 5140000 | 4860000 | HERBALPOWDER20MT.ESSENTIALOIL14000LITREDRIEDHE... | 34 | AGRO AND FORESTRY | SMALL | 100 | Local - 40%, Foreign - 60% | 4 | 2078 | GANDAKI | 0.066 | 3 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_AGRO AND FORESTRY_LARGE: (Empty) DataFrame for GANDAKI_AGRO AND FORESTRY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 37 | ANNAPURNARESEARCHCENTERANDFARMING | NAWALPARASI | 191431250 | 184931250 | 6500000 | CHICKEN192000KGCHICKS432000NOSEGGS1500000NOSPO... | 36 | AGRO AND FORESTRY | MEDIUM | 200 | Local - 100%, Foreign - 0% | 6 | 2076 | GANDAKI | 0.066 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 190 | POKHARABHANJYANGMULTIAGRICULTURE | TANAHU | 250000000 | 237000000 | 13000000 | VEGETABLES450M.T.PIG20M.T.FISH20M.T.MILK210KL. | 35 | AGRO AND FORESTRY | MEDIUM | 20 | Local - 100%, Foreign - 0% | 11 | 2076 | GANDAKI | 0.066 | 2 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_MINERAL_SMALL: (Empty) DataFrame for GANDAKI_MINERAL_LARGE: (Empty) DataFrame for GANDAKI_MINERAL_MEDIUM: (Empty) DataFrame for GANDAKI_INFRASTRUCTURE_SMALL: (Empty) DataFrame for GANDAKI_INFRASTRUCTURE_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 354 | JAYABHADRAKALIDEVELOPERS | KASKI | 1574442000 | 1557369750 | 17072250 | RESIDENTIALAPARTMENTS267964SQ.FT. | 18 | INFRASTRUCTURE | LARGE | 700 | Local - 100%, Foreign - 0% | 9 | 2077 | GANDAKI | 0.004 | 1 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for GANDAKI_INFRASTRUCTURE_MEDIUM: (Empty) DataFrame for LUMBINI_SERVICE_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 322 | RUPANDEHIC.T.ANDDIAGNOSISCENTER | RUPANDEHI | 45300000 | 38200000 | 7100000 | C.T.SCANSERVICE15000CASES | 17 | SERVICE | SMALL | 100 | Local - 100%, Foreign - 0% | 7 | 2077 | LUMBINI | 0.166 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_SERVICE_LARGE: (Empty) DataFrame for LUMBINI_SERVICE_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 402 | ANNAPURNACORPORATION | RUPANDEHI | 283771900 | 181675000 | 102096900 | CHAMAL5605M.T.DALL693\nM.T.GEDAGUD1770M.T.MASA... | 23 | SERVICE | MEDIUM | 20 | Local - 100%, Foreign - 0% | 12 | 2077 | LUMBINI | 0.166 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 496 | GHORAHICEMENTINDUSTRYLIMITED(CEMENTPACKEGING) | BANKE | 210338607 | 167189200 | 43149407 | CEMENT120000MT. | 48 | SERVICE | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 5 | 2078 | LUMBINI | 0.166 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_ENERGY_SMALL: (Empty) DataFrame for LUMBINI_ENERGY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 175 | SHANGRILAHYDROPOWER | RUKUM | 4998000000 | 4938360000 | 59640000 | àœà2àpà¿à°|à¥Öà°*à¥Öà°21M.W. | 57 | ENERGY | LARGE | 50 | Local - 100%, Foreign - 0% | 10 | 2076 | LUMBINI | 0.144 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 278 | POSITIVEENERGY | KAPILBASTU | 945000000 | 941000000 | 4000000 | SOLARELECTRICITY10M.W. | 10 | ENERGY | LARGE | 5 | Local - 100%, Foreign - 0% | 5 | 2077 | LUMBINI | 0.144 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_ENERGY_MEDIUM: (Empty) DataFrame for LUMBINI_INFORMATION TECHNOLOGY_SMALL: (Empty) DataFrame for LUMBINI_INFORMATION TECHNOLOGY_LARGE: (Empty) DataFrame for LUMBINI_INFORMATION TECHNOLOGY_MEDIUM: (Empty) DataFrame for LUMBINI_TOURISM_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 258 | NIRVANALUXURYINTERNATINOAL | RUPANDEHI | 160000000 | 110000000 | 50000000 | CASINOPLAYERS(FOREIGNERS0NLY)73000PERSONS | 54 | TOURISM | SMALL | 150 | Local - 100%, Foreign - 0% | 4 | 2077 | LUMBINI | 0.258 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 395 | SIDDARTHAGAMINGZONE | RUPANDEHI | 150000000 | 101000000 | 49000000 | CASINOPLAYERS/GUESTS(FOREIGNONLY)91250PERSONS | 63 | TOURISM | SMALL | 200 | Local - 100%, Foreign - 0% | 12 | 2077 | LUMBINI | 0.258 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_TOURISM_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 106 | ASIANINTERNATIONALREGENCY | RUPANDEHI | 2380000000 | 2340000000 | 40000000 | HOTEL176BEDSRESTAURANT225SEATS | 97 | TOURISM | LARGE | 2000 | Local - 100%, Foreign - 0% | 8 | 2076 | LUMBINI | 0.258 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 202 | SIPRABHYAHOTELSANDRES0RTS(1) | BARDIYA | 1300000000 | 1250000000 | 50000000 | HOTEL54BEDSRESTAURANT40SEATS | 75 | TOURISM | LARGE | 1500 | Local - 100%, Foreign - 0% | 11 | 2076 | LUMBINI | 0.258 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_TOURISM_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 425 | HOTELMEGASECONDEDITION | RUPANDEHI | 391798454 | 358217150 | 33581304 | HOTEL130BEDRESTAURANT180SEAT | 135 | TOURISM | MEDIUM | 2000 | Local - 100%, Foreign - 0% | 1 | 2078 | LUMBINI | 0.258 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 336 | DIVYANEPALAYURVEDAPHARMACY | RUPANDEHI | 125000000 | 105000000 | 20000000 | VATI/GUGGUL/CAPSULE/TABLET900000KG.SYRUP/ASVA\... | 35 | MANUFACTURING | SMALL | 500 | Local - 100%, Foreign - 0% | 8 | 2077 | LUMBINI | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 375 | TIRUPATIMETALINDUSTRIES | RUPANDEHI | 641000000 | 141000000 | 500000000 | LEADINGOT12000M.T. | 111 | MANUFACTURING | SMALL | 250 | Local - 100%, Foreign - 0% | 11 | 2077 | LUMBINI | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_MANUFACTURING_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 270 | MEXOTILES | KAPILBASTU | 667897700 | 578852500 | 89045200 | FLOORTILES16000000PIECESWALLTILES12500000PIECES | 114 | MANUFACTURING | LARGE | 2000 | Local - 100%, Foreign - 0% | 4 | 2077 | LUMBINI | 0.31 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 390 | TEJSUBEVERAGES | RUPANDEHI | 1500000000 | 800000000 | 700000000 | JUICE18000KL.CARBONATEDSOFTDRINK18000KL.ENERGY... | 150 | MANUFACTURING | LARGE | 1000 | Local - 100%, Foreign - 0% | 12 | 2077 | LUMBINI | 0.31 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | MODERNDOORS&WOODPRODUCTS | BANKE | 248846000 | 155996000 | 92850000 | PLYWOODFALSEDOOR6000000SQUAREMETER\nDECORATEDB... | 124 | MANUFACTURING | MEDIUM | 500 | Local - 100%, Foreign - 0% | 5 | 2076 | LUMBINI | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 50 | ATALPLYWOODINDUSTRIES | KAPILBASTU | 233000000 | 185000000 | 48000000 | PLYWOOD4500000SQ.FT.FALSEDOOR3000000SQ.FT. | 87 | MANUFACTURING | MEDIUM | 500 | Local - 100%, Foreign - 0% | 6 | 2076 | LUMBINI | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_AGRO AND FORESTRY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 384 | ARAMBHAAGR0INDUSTRIES | DANG | 135000000 | 110000000 | 25000000 | GINGERSLICE500M.T.GINGERCANDY200M.T.TURMERICSL... | 29 | AGRO AND FORESTRY | SMALL | 100 | Local - 100%, Foreign - 0% | 11 | 2077 | LUMBINI | 0.066 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_AGRO AND FORESTRY_LARGE: (Empty) DataFrame for LUMBINI_AGRO AND FORESTRY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 428 | O.C.B.PELLETFEED | BANKE | 615000000 | 405000000 | 210000000 | POULTRYFEED80000MT.FISHFEED20000MT.CATTLEFEED2... | 83 | AGRO AND FORESTRY | MEDIUM | 2000 | Local - 100%, Foreign - 0% | 1 | 2078 | LUMBINI | 0.066 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 495 | AGRIVASTUCOLDST0RAGE | KAPILBASTU | 234192207 | 160104320 | 74087887 | COLDST0RAGE1800MT.PRODUCTIONPROCESSINGAND\nSTO... | 27 | AGRO AND FORESTRY | MEDIUM | 300 | Local - 100%, Foreign - 0% | 5 | 2078 | LUMBINI | 0.066 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
DataFrame for LUMBINI_MINERAL_SMALL: (Empty) DataFrame for LUMBINI_MINERAL_LARGE: (Empty) DataFrame for LUMBINI_MINERAL_MEDIUM: (Empty) DataFrame for LUMBINI_INFRASTRUCTURE_SMALL: (Empty) DataFrame for LUMBINI_INFRASTRUCTURE_LARGE: (Empty) DataFrame for LUMBINI_INFRASTRUCTURE_MEDIUM: (Empty) DataFrame for MADHESH_SERVICE_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | ASHWINIENGINEERINGNEPAL | SIRAHA | 50000000 | 28950000 | 21050000 | VARIOUSKINDS0FCONSTRUCTIONWORKS(CONSTRUCTIONRE... | 68 | SERVICE | SMALL | 10 | Local - 0%, Foreign - 100% | 8 | 2076 | MADHESH | 0.166 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_SERVICE_LARGE: (Empty) DataFrame for MADHESH_SERVICE_MEDIUM: (Empty) DataFrame for MADHESH_ENERGY_SMALL: (Empty) DataFrame for MADHESH_ENERGY_LARGE: (Empty) DataFrame for MADHESH_ENERGY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | SAGARMATHAENERTY&CONSTRUCTION | DHANUSHA | 296000000 | 290000000 | 6000000 | SOLAR(PV)3M.W. | 18 | ENERGY | MEDIUM | 10 | Local - 100%, Foreign - 0% | 11 | 2076 | MADHESH | 0.144 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_INFORMATION TECHNOLOGY_SMALL: (Empty) DataFrame for MADHESH_INFORMATION TECHNOLOGY_LARGE: (Empty) DataFrame for MADHESH_INFORMATION TECHNOLOGY_MEDIUM: (Empty) DataFrame for MADHESH_TOURISM_SMALL: (Empty) DataFrame for MADHESH_TOURISM_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 75 | HOTELICHCHHA | BARA | 559000000 | 534000000 | 25000000 | HOTEL96BEDSRESTAURANT200SEATS | 100 | TOURISM | LARGE | 400 | Local - 100%, Foreign - 0% | 7 | 2076 | MADHESH | 0.258 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_TOURISM_MEDIUM: (Empty) DataFrame for MADHESH_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | RANGPAL | BARA | 10000000 | 5500000 | 4500000 | INCENSESTICKS7000KGVERMILLION(SINDO0R)700\nKGT... | 28 | MANUFACTURING | SMALL | 25 | Local - 0%, Foreign - 100% | 7 | 2076 | MADHESH | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 185 | ANNAPURNALEATHERTANNINGINDUSTRY | BARA | 100000000 | 57000000 | 43000000 | WET-BLUE:600000SQ.FT.CRUSTLEATHER:375000SQ.FT.... | 38 | MANUFACTURING | SMALL | 200 | Local - 0%, Foreign - 100% | 10 | 2076 | MADHESH | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_MANUFACTURING_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 | HIMALASIMSTEEL | PARSA | 4944089000 | 2472400000 | 2471689000 | GIWIRE25000M.T.MSANGLE/CHANNEL/BEAM50000M.T.MS... | 550 | MANUFACTURING | LARGE | 75000 | Local - 100%, Foreign - 0% | 6 | 2076 | MADHESH | 0.31 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 227 | NEPALCERAMICINDUSTRY | BARA | 662000000 | 595732240 | 66267760 | FL0ORTILES28266000SQ.FT. | 200 | MANUFACTURING | LARGE | 500 | Local - 100%, Foreign - 0% | 2 | 2077 | MADHESH | 0.31 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | PRESTIGETEXTILEINDUSTRIES | PARSA | 248112790 | 148062534 | 100050256 | WOVENFABRIC2350000METERKNITTEDFABRIC135TON | 71 | MANUFACTURING | MEDIUM | 500 | Local - 100%, Foreign - 0% | 5 | 2076 | MADHESH | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 47 | VIJAYROSHANSTEELINDUSTRIES | PARSA | 223500000 | 143500000 | 80000000 | SSDINNERANDLAUNCHPLATE2000M.T.SSBOULANDMUG\n80... | 45 | MANUFACTURING | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 6 | 2076 | MADHESH | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_AGRO AND FORESTRY_SMALL: (Empty) DataFrame for MADHESH_AGRO AND FORESTRY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 284 | JANAKPURAGR0FARM | DHANUSHA | 1042000000 | 900000000 | 142000000 | EGGS129405097PIECESRETIREDBIRDS250000K.G.\n0RG... | 350 | AGRO AND FORESTRY | LARGE | 1000 | Local - 100%, Foreign - 0% | 5 | 2077 | MADHESH | 0.066 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for MADHESH_AGRO AND FORESTRY_MEDIUM: (Empty) DataFrame for MADHESH_MINERAL_SMALL: (Empty) DataFrame for MADHESH_MINERAL_LARGE: (Empty) DataFrame for MADHESH_MINERAL_MEDIUM: (Empty) DataFrame for MADHESH_INFRASTRUCTURE_SMALL: (Empty) DataFrame for MADHESH_INFRASTRUCTURE_LARGE: (Empty) DataFrame for MADHESH_INFRASTRUCTURE_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 408 | EMPERORDEVELOPERS | MAHOTTARI | 163873000 | 155873000 | 8000000 | RESIDENTIALAPARTMENTS100000SQ.FT. | 27 | INFRASTRUCTURE | MEDIUM | 500 | Local - 100%, Foreign - 0% | 12 | 2077 | MADHESH | 0.004 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
DataFrame for KARNALI_SERVICE_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 256 | PRISTINENEPALTERMINALS | KALIKOT | 80000000 | 64000000 | 16000000 | CONTAINERS14550MTBULK&BREAKBULK9050MT | 74 | SERVICE | SMALL | 100 | Local - 36%, Foreign - 64% | 4 | 2077 | KARNALI | 0.166 | 3 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KARNALI_SERVICE_LARGE: (Empty) DataFrame for KARNALI_SERVICE_MEDIUM: (Empty) DataFrame for KARNALI_ENERGY_SMALL: (Empty) DataFrame for KARNALI_ENERGY_LARGE:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 309 | SETIKHOLAHYDROPWER | KALIKOT | 5005648000 | 4945816000 | 59832000 | HYDROELECTRICITY22M.W. | 36 | ENERGY | LARGE | 100 | Local - 100%, Foreign - 0% | 6 | 2077 | KARNALI | 0.144 | 1 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 432 | MANDAKINIHYDROPOWERCOMPANYLIMITED-1 | JAJARKOT | 569981000 | 565307297 | 4673703 | HYDROELECTRICITY2.9M.W. | 40 | ENERGY | LARGE | 20 | Local - 100%, Foreign - 0% | 1 | 2078 | KARNALI | 0.144 | 1 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KARNALI_ENERGY_MEDIUM: (Empty) DataFrame for KARNALI_INFORMATION TECHNOLOGY_SMALL: (Empty) DataFrame for KARNALI_INFORMATION TECHNOLOGY_LARGE: (Empty) DataFrame for KARNALI_INFORMATION TECHNOLOGY_MEDIUM: (Empty) DataFrame for KARNALI_TOURISM_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | SPRINGHILLHOTEL | HUMLA | 50000000 | 47000000 | 3000000 | HOTEL22BEDSRESTAURANT40SEATS | 29 | TOURISM | SMALL | 60 | Local - 0%, Foreign - 100% | 8 | 2076 | KARNALI | 0.258 | 3 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 285 | SAMADHIVILLAGE | SALYAN | 50000000 | 37500000 | 12500000 | HOTEL20BEDSRESTAURANT100SEATS | 17 | TOURISM | SMALL | 50 | Local - 0%, Foreign - 100% | 6 | 2077 | KARNALI | 0.258 | 3 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KARNALI_TOURISM_LARGE: (Empty) DataFrame for KARNALI_TOURISM_MEDIUM: (Empty) DataFrame for KARNALI_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 250 | NEBULAENERGY | HUMLA | 150000000 | 105000000 | 45000000 | ELECTRICVEHICLESASSEMBLING300NOS. | 67 | MANUFACTURING | SMALL | 500 | Local - 100%, Foreign - 0% | 3 | 2077 | KARNALI | 0.31 | 3 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 434 | ADIRATEXTILEINDUSTRIES | KALIKOT | 200000000 | 137100000 | 62900000 | VARIOUSFABRICS4200000SQ.M | 59 | MANUFACTURING | SMALL | 800 | Local - 100%, Foreign - 0% | 1 | 2078 | KARNALI | 0.31 | 3 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KARNALI_MANUFACTURING_LARGE: (Empty) DataFrame for KARNALI_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 249 | HIMALAYANRENEWABLEOILINDUSTRY | DOLPA | 250000000 | 224492402 | 25507598 | PETROL-1485KLDIESEL-1485KLLUBRICANTS(BYPRODUCT... | 42 | MANUFACTURING | MEDIUM | 2000 | Local - 20%, Foreign - 80% | 3 | 2077 | KARNALI | 0.31 | 2 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 342 | PATHIVARAMATAFERTILIZERINDUSTRIES | JAJARKOT | 219800000 | 162400000 | 57400000 | CHEMICALFERTILIZER3000M.T. | 45 | MANUFACTURING | MEDIUM | 900 | Local - 100%, Foreign - 0% | 9 | 2077 | KARNALI | 0.31 | 2 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KARNALI_AGRO AND FORESTRY_SMALL: (Empty) DataFrame for KARNALI_AGRO AND FORESTRY_LARGE: (Empty) DataFrame for KARNALI_AGRO AND FORESTRY_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 351 | BOLBOMFEEDINDUSTRIES | KALIKOT | 340000000 | 213000000 | 127000000 | ANIMALFEED(PELLET/DISC)73000M.T. | 55 | AGRO AND FORESTRY | MEDIUM | 1000 | Local - 100%, Foreign - 0% | 9 | 2077 | KARNALI | 0.066 | 2 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
DataFrame for KARNALI_MINERAL_SMALL: (Empty) DataFrame for KARNALI_MINERAL_LARGE: (Empty) DataFrame for KARNALI_MINERAL_MEDIUM: (Empty) DataFrame for KARNALI_INFRASTRUCTURE_SMALL: (Empty) DataFrame for KARNALI_INFRASTRUCTURE_LARGE: (Empty) DataFrame for KARNALI_INFRASTRUCTURE_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_SERVICE_SMALL: (Empty) DataFrame for SUDUR-PASCHIM_SERVICE_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_SERVICE_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_ENERGY_SMALL: (Empty) DataFrame for SUDUR-PASCHIM_ENERGY_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_ENERGY_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_INFORMATION TECHNOLOGY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 176 | TALKSUREINC. | KAILALI | 50000000 | 44000000 | 6000000 | SOFTWAREDEVELOPMENT300PACKAGES | 45 | INFORMATION TECHNOLOGY | SMALL | 100 | Local - 0%, Foreign - 100% | 10 | 2076 | SUDUR-PASCHIM | 0.05 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
DataFrame for SUDUR-PASCHIM_INFORMATION TECHNOLOGY_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_INFORMATION TECHNOLOGY_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_TOURISM_SMALL: (Empty) DataFrame for SUDUR-PASCHIM_TOURISM_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_TOURISM_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_MANUFACTURING_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 323 | KAILALIWIREANDSTEELINDUSTRIES | KAILALI | 150000000 | 105000000 | 45000000 | GIWIRE6000M.T.GABIONMATTRESS6000M.T. | 70 | MANUFACTURING | SMALL | 2000 | Local - 100%, Foreign - 0% | 7 | 2077 | SUDUR-PASCHIM | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 368 | SHIVASHAKTIBALUAPRASODHANKENDRA | KAILALI | 16424900 | 11474900 | 4950000 | PROCESSEDSAND11000M.T. | 18 | MANUFACTURING | SMALL | 100 | Local - 100%, Foreign - 0% | 10 | 2077 | SUDUR-PASCHIM | 0.31 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
DataFrame for SUDUR-PASCHIM_MANUFACTURING_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_MANUFACTURING_MEDIUM:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 436 | SHEETALINDUSTRIES | KAILALI | 311540500 | 261443000 | 50097500 | OXYGENLIQUID2742857CU.M.OXYGENGAS1097143CU.M.N... | 40 | MANUFACTURING | MEDIUM | 1500 | Local - 100%, Foreign - 0% | 1 | 2078 | SUDUR-PASCHIM | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 482 | MALIKASTEELS | KAILALI | 411067075 | 254882075 | 156185000 | MSRIBBEDBAR30000MTGIWIRE8000MT | 285 | MANUFACTURING | MEDIUM | 2000 | Local - 100%, Foreign - 0% | 4 | 2078 | SUDUR-PASCHIM | 0.31 | 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
DataFrame for SUDUR-PASCHIM_AGRO AND FORESTRY_SMALL:
| INDUSTRY NAME | DISTRICT | TOTAL CAPITAL | FIXED CAPITAL | WORKING CAPITAL | PRODUCT AND ANNUAL CAPACITY | EMPLOYMENT | CATEGORY | SCALE | POWER | % OF INVESTMENT | MONTH | YEAR | PROVINCE | CATEGORY_FREQ | SCALE_encode | PROVINCE_BAGMATI | PROVINCE_GANDAKI | PROVINCE_KARNALI | PROVINCE_KOSHI | PROVINCE_LUMBINI | PROVINCE_MADHESH | PROVINCE_SUDUR-PASCHIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 341 | SHOVAAGR0ANDRESEARCHCENTER | BAJURA | 150000000 | 145000000 | 5000000 | APPLE600M.T.WALNUT200M.T.HERBS(BOJHOGURJOETC.)... | 264 | AGRO AND FORESTRY | SMALL | 200 | Local - 100%, Foreign - 0% | 8 | 2077 | SUDUR-PASCHIM | 0.066 | 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
DataFrame for SUDUR-PASCHIM_AGRO AND FORESTRY_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_AGRO AND FORESTRY_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_MINERAL_SMALL: (Empty) DataFrame for SUDUR-PASCHIM_MINERAL_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_MINERAL_MEDIUM: (Empty) DataFrame for SUDUR-PASCHIM_INFRASTRUCTURE_SMALL: (Empty) DataFrame for SUDUR-PASCHIM_INFRASTRUCTURE_LARGE: (Empty) DataFrame for SUDUR-PASCHIM_INFRASTRUCTURE_MEDIUM: (Empty)
In [105]:
# @title make visualization based on employment
fig = px.violin(df0, y="EMPLOYMENT", x="CATEGORY", color="CATEGORY", box=True, points="all",
title="Violin Plot of Employment by Category")
fig.show()
In [106]:
# @title Employment Generation per Province:
province_summary = df0.groupby(['PROVINCE']).agg({'EMPLOYMENT': 'sum'})
province_summary = province_summary.sort_values(by='EMPLOYMENT', ascending=False)
display(province_summary)
fig = px.bar(province_summary, x=province_summary.index, y='EMPLOYMENT',
title='Employment by province')
fig.show()
| EMPLOYMENT | |
|---|---|
| PROVINCE | |
| BAGMATI | 16700 |
| KOSHI | 3761 |
| MADHESH | 3537 |
| LUMBINI | 3176 |
| GANDAKI | 3157 |
| SUDUR-PASCHIM | 977 |
| KARNALI | 651 |
In [107]:
# @title Combined Analysis (using multiple aggregation methods)
combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
total_capital_invested=('TOTAL CAPITAL', 'sum'),
avg_employment=('EMPLOYMENT', 'mean')
)
# Display the resulting DataFrame
display(combined_analysis)
| total_capital_invested | avg_employment | ||
|---|---|---|---|
| CATEGORY | PROVINCE | ||
| AGRO AND FORESTRY | BAGMATI | 1445000000 | 52.076923 |
| GANDAKI | 1557346614 | 57.142857 | |
| KARNALI | 340000000 | 55.000000 | |
| KOSHI | 1515642000 | 79.714286 | |
| LUMBINI | 984192207 | 46.333333 | |
| MADHESH | 1042000000 | 350.000000 | |
| SUDUR-PASCHIM | 150000000 | 264.000000 | |
| ENERGY | BAGMATI | 53992062524 | 67.294118 |
| GANDAKI | 57205948565 | 33.400000 | |
| KARNALI | 7475629000 | 35.666667 | |
| KOSHI | 79389736445 | 47.333333 | |
| LUMBINI | 5943000000 | 33.500000 | |
| MADHESH | 296000000 | 18.000000 | |
| INFORMATION TECHNOLOGY | BAGMATI | 3790140358 | 96.333333 |
| SUDUR-PASCHIM | 50000000 | 45.000000 | |
| INFRASTRUCTURE | GANDAKI | 1574442000 | 18.000000 |
| MADHESH | 163873000 | 27.000000 | |
| MANUFACTURING | BAGMATI | 10318253800 | 69.708333 |
| GANDAKI | 5807849694 | 72.076923 | |
| KARNALI | 1719800000 | 55.166667 | |
| KOSHI | 7204402915 | 61.272727 | |
| LUMBINI | 12911175152 | 97.227273 | |
| MADHESH | 17262109248 | 82.611111 | |
| SUDUR-PASCHIM | 1613977012 | 83.500000 | |
| MINERAL | BAGMATI | 123446032 | 92.000000 |
| SERVICE | BAGMATI | 12589005914 | 72.563380 |
| GANDAKI | 728400000 | 46.600000 | |
| KARNALI | 80000000 | 74.000000 | |
| KOSHI | 665600000 | 54.000000 | |
| LUMBINI | 539410507 | 29.333333 | |
| MADHESH | 50000000 | 68.000000 | |
| TOURISM | BAGMATI | 20349736834 | 42.763441 |
| GANDAKI | 4943781796 | 45.875000 | |
| KARNALI | 153000000 | 28.000000 | |
| KOSHI | 1928091500 | 76.375000 | |
| LUMBINI | 6239083454 | 92.875000 | |
| MADHESH | 559000000 | 100.000000 |
In [108]:
# @title Combined Analysis (using multiple aggregation methods)
combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
avg_employment=('EMPLOYMENT', 'mean')
).reset_index() # Reset index to have 'CATEGORY' and 'PROVINCE' as columns
# Set 'CATEGORY' and 'PROVINCE' as index
combined_analysis = combined_analysis.set_index(['CATEGORY', 'PROVINCE'])
# Display the resulting DataFrame
display(combined_analysis)
| avg_employment | ||
|---|---|---|
| CATEGORY | PROVINCE | |
| AGRO AND FORESTRY | BAGMATI | 52.076923 |
| GANDAKI | 57.142857 | |
| KARNALI | 55.000000 | |
| KOSHI | 79.714286 | |
| LUMBINI | 46.333333 | |
| MADHESH | 350.000000 | |
| SUDUR-PASCHIM | 264.000000 | |
| ENERGY | BAGMATI | 67.294118 |
| GANDAKI | 33.400000 | |
| KARNALI | 35.666667 | |
| KOSHI | 47.333333 | |
| LUMBINI | 33.500000 | |
| MADHESH | 18.000000 | |
| INFORMATION TECHNOLOGY | BAGMATI | 96.333333 |
| SUDUR-PASCHIM | 45.000000 | |
| INFRASTRUCTURE | GANDAKI | 18.000000 |
| MADHESH | 27.000000 | |
| MANUFACTURING | BAGMATI | 69.708333 |
| GANDAKI | 72.076923 | |
| KARNALI | 55.166667 | |
| KOSHI | 61.272727 | |
| LUMBINI | 97.227273 | |
| MADHESH | 82.611111 | |
| SUDUR-PASCHIM | 83.500000 | |
| MINERAL | BAGMATI | 92.000000 |
| SERVICE | BAGMATI | 72.563380 |
| GANDAKI | 46.600000 | |
| KARNALI | 74.000000 | |
| KOSHI | 54.000000 | |
| LUMBINI | 29.333333 | |
| MADHESH | 68.000000 | |
| TOURISM | BAGMATI | 42.763441 |
| GANDAKI | 45.875000 | |
| KARNALI | 28.000000 | |
| KOSHI | 76.375000 | |
| LUMBINI | 92.875000 | |
| MADHESH | 100.000000 |
In [109]:
# @title Average Employment by Category and Province
combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
avg_employment=('EMPLOYMENT', 'mean')
).reset_index()
display(combined_analysis)
fig = px.bar(combined_analysis,
x="CATEGORY",
y="avg_employment",
color="PROVINCE",
title="Average Employment by Category and Province",
barmode='group',
text='avg_employment')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
| CATEGORY | PROVINCE | avg_employment | |
|---|---|---|---|
| 0 | AGRO AND FORESTRY | BAGMATI | 52.076923 |
| 1 | AGRO AND FORESTRY | GANDAKI | 57.142857 |
| 2 | AGRO AND FORESTRY | KARNALI | 55.000000 |
| 3 | AGRO AND FORESTRY | KOSHI | 79.714286 |
| 4 | AGRO AND FORESTRY | LUMBINI | 46.333333 |
| 5 | AGRO AND FORESTRY | MADHESH | 350.000000 |
| 6 | AGRO AND FORESTRY | SUDUR-PASCHIM | 264.000000 |
| 7 | ENERGY | BAGMATI | 67.294118 |
| 8 | ENERGY | GANDAKI | 33.400000 |
| 9 | ENERGY | KARNALI | 35.666667 |
| 10 | ENERGY | KOSHI | 47.333333 |
| 11 | ENERGY | LUMBINI | 33.500000 |
| 12 | ENERGY | MADHESH | 18.000000 |
| 13 | INFORMATION TECHNOLOGY | BAGMATI | 96.333333 |
| 14 | INFORMATION TECHNOLOGY | SUDUR-PASCHIM | 45.000000 |
| 15 | INFRASTRUCTURE | GANDAKI | 18.000000 |
| 16 | INFRASTRUCTURE | MADHESH | 27.000000 |
| 17 | MANUFACTURING | BAGMATI | 69.708333 |
| 18 | MANUFACTURING | GANDAKI | 72.076923 |
| 19 | MANUFACTURING | KARNALI | 55.166667 |
| 20 | MANUFACTURING | KOSHI | 61.272727 |
| 21 | MANUFACTURING | LUMBINI | 97.227273 |
| 22 | MANUFACTURING | MADHESH | 82.611111 |
| 23 | MANUFACTURING | SUDUR-PASCHIM | 83.500000 |
| 24 | MINERAL | BAGMATI | 92.000000 |
| 25 | SERVICE | BAGMATI | 72.563380 |
| 26 | SERVICE | GANDAKI | 46.600000 |
| 27 | SERVICE | KARNALI | 74.000000 |
| 28 | SERVICE | KOSHI | 54.000000 |
| 29 | SERVICE | LUMBINI | 29.333333 |
| 30 | SERVICE | MADHESH | 68.000000 |
| 31 | TOURISM | BAGMATI | 42.763441 |
| 32 | TOURISM | GANDAKI | 45.875000 |
| 33 | TOURISM | KARNALI | 28.000000 |
| 34 | TOURISM | KOSHI | 76.375000 |
| 35 | TOURISM | LUMBINI | 92.875000 |
| 36 | TOURISM | MADHESH | 100.000000 |
In [110]:
# @title a bar chart showing total capital invested by category
combined_analysis = df0.groupby(['CATEGORY', 'PROVINCE']).agg(
total_capital_invested=('TOTAL CAPITAL', 'sum'),
avg_employment=('EMPLOYMENT', 'mean')
).reset_index()
fig = px.bar(combined_analysis, x='CATEGORY', y='total_capital_invested', color='PROVINCE',
title='Total Capital Invested by Category and Province',
labels={'total_capital_invested': 'Total Capital Invested', 'CATEGORY': 'Category', 'PROVINCE': 'Province'})
fig.show()
In [111]:
# @title scatter plot showing the relationship between total capital invested and average employment
fig = px.scatter(combined_analysis, x='total_capital_invested', y='avg_employment', color='CATEGORY',
title='Relationship between Total Capital Invested and Average Employment',
labels={'total_capital_invested': 'Total Capital Invested', 'avg_employment': 'Average Employment', 'CATEGORY': 'Category'},
hover_data=['PROVINCE']) # show province when hovering over data points
fig.show()
In [112]:
def create_employment_diagram(df):
# group data by province and district, summing employment
employment_by_district_province = df.groupby(['PROVINCE', 'DISTRICT'])['EMPLOYMENT'].sum().reset_index()
# create the stacked bar chart
fig = go.Figure()
for province in employment_by_district_province['PROVINCE'].unique():
province_data = employment_by_district_province[employment_by_district_province['PROVINCE'] == province]
fig.add_trace(go.Bar(
x=province_data['DISTRICT'],
y=province_data['EMPLOYMENT'],
name=province,
text=province_data['EMPLOYMENT'], # display employment values on bars
textposition='auto'
))
fig.update_layout(
barmode='stack', # stack bars for each province
title='Employment by District and Province',
xaxis_title='District',
yaxis_title='Employment',
xaxis={'categoryorder':'total descending'}, # Order districts by total employment
width=1000, # adjust width
height=600, # adjust height
legend_title='Province',
)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside') #show employment values outside the bar
return fig
fig = create_employment_diagram(df0)
fig.show()
In [113]:
def create_capital_diagram(df):
# group data by province, category, and scale, summing total capital
capital_data = df.groupby(['PROVINCE', 'CATEGORY', 'SCALE'])['TOTAL CAPITAL'].sum().reset_index()
display(capital_data)
# create subplots
num_provinces = len(capital_data['PROVINCE'].unique())
rows = (num_provinces + 2) // 3 # calculate rows needed, ensuring at least 1 row
cols = min(num_provinces, 3) # limit columns to 3
fig = make_subplots(rows=rows, cols=cols, subplot_titles=capital_data['PROVINCE'].unique(), shared_xaxes=False)
subplot_index = 1
for province in capital_data['PROVINCE'].unique():
province_data = capital_data[capital_data['PROVINCE'] == province]
row = (subplot_index - 1) // cols + 1
col = (subplot_index - 1) % cols + 1
for category in province_data['CATEGORY'].unique():
category_data = province_data[province_data['CATEGORY'] == category]
fig.add_trace(go.Bar(
x=category_data['SCALE'],
y=category_data['TOTAL CAPITAL'],
name=category,
text=category_data['TOTAL CAPITAL'],
textposition='auto'
), row=row, col=col)
subplot_index += 1
fig.update_layout(
barmode='stack',
title='Total Capital by Province, Category, and Scale',
width=1200,
height=800,
showlegend=True,
title_x=0.5, # center the title
margin=dict(l=50, r=50, t=100, b=50) # adjust margins for better spacing
)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_xaxes(tickangle=45, tickfont=dict(size=10)) # rotate x-axis labels for better visibility
return fig
fig = create_capital_diagram(df0)
fig.show()
| PROVINCE | CATEGORY | SCALE | TOTAL CAPITAL | |
|---|---|---|---|---|
| 0 | BAGMATI | AGRO AND FORESTRY | MEDIUM | 710000000 |
| 1 | BAGMATI | AGRO AND FORESTRY | SMALL | 735000000 |
| 2 | BAGMATI | ENERGY | LARGE | 53148711875 |
| 3 | BAGMATI | ENERGY | MEDIUM | 725850649 |
| 4 | BAGMATI | ENERGY | SMALL | 117500000 |
| ... | ... | ... | ... | ... |
| 67 | MADHESH | TOURISM | LARGE | 559000000 |
| 68 | SUDUR-PASCHIM | AGRO AND FORESTRY | SMALL | 150000000 |
| 69 | SUDUR-PASCHIM | INFORMATION TECHNOLOGY | SMALL | 50000000 |
| 70 | SUDUR-PASCHIM | MANUFACTURING | MEDIUM | 959207641 |
| 71 | SUDUR-PASCHIM | MANUFACTURING | SMALL | 654769371 |
72 rows × 4 columns